├── shapefile ├── MEIC-0P25.cpg ├── CN-Province.dbf ├── CN-Province.sbn ├── CN-Province.sbx ├── CN-Province.shp ├── CN-Province.shx ├── MEIC-0P25.dbf ├── MEIC-0P25.sbn ├── MEIC-0P25.sbx ├── MEIC-0P25.shp ├── MEIC-0P25.shx ├── MEIC-0P25.prj └── CN-Province.prj ├── Doc ├── 7-1.png ├── 7-2.png ├── 7-3.png ├── 7-4.png ├── 7-5.png ├── 7-6.png ├── zonalst_nox.png ├── monthly_profile.png ├── speices_warning.png ├── year2month_output.png ├── meic2geotiff_output.png ├── meic_2017_PMcoarse.png ├── original_meic_files.png ├── converted_meic_files.png ├── how_to_treat_the_emssion_which_resolution_is_fine_cn.md ├── how_to_do_vertical_allocation_cn.md ├── how_to_use_shapefile_for_mask_cn.md ├── how_to_treat_the_yearly_emission_cn.md ├── how_to_combine_meic_and_mix_cn.md ├── adopt_local_emission_to_meic_cn.md └── adopt_meic_for_prd_emission_file_cn.md ├── .gitattributes ├── UTIL ├── combine │ ├── src.pyd │ └── combine.py └── rename_original_inventory │ ├── src.pyd │ ├── README.md │ └── rename_original_inventory_(pollutant).py ├── requirement.txt ├── profile.csv ├── input ├── GRIDDESC.CN27km └── GRIDDESC.PRD274x181 ├── temporal ├── weekly.csv ├── monthly.csv └── hourly.csv ├── coarse_emission_2_fine_emission.py ├── Create-CMAQ-Emission-File.py ├── fine_emission_2_coarse_emission.py ├── vertical_allocation.py ├── allocator └── README.md ├── Create_CMAQ_mask.py ├── .gitignore ├── namelist.input ├── README.rst ├── species ├── MEIC-CB05_CB06_speciate_agriculture.csv ├── MEIC-CB05_CB06_speciate_industry.csv ├── MEIC-CB05_CB06_speciate_power.csv ├── MEIC-CB05_CB06_speciate_residential.csv ├── MEIC-CB05_CB06_speciate_transportation.csv ├── MEIC-SAPRC07_SAPRC07_speciate_power.csv ├── MEIC-SAPRC07_SAPRC07_speciate_agriculture.csv ├── MEIC-SAPRC07_SAPRC07_speciate_industry.csv ├── MEIC-SAPRC07_SAPRC07_speciate_residential.csv └── MEIC-SAPRC07_SAPRC07_speciate_transportation.csv ├── README.CN.md ├── PREP ├── heic_2_GeoTiff.py ├── meic_2_GeoTiff.py ├── mix_2_GeoTiff.py └── CEDSv2_2_GeoTiff.py ├── year2month.py ├── calculate-pmc.py └── src.py /shapefile/MEIC-0P25.cpg: -------------------------------------------------------------------------------- 1 | UTF-8 -------------------------------------------------------------------------------- /Doc/7-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Airwhf/MEIAT-CMAQ/HEAD/Doc/7-1.png -------------------------------------------------------------------------------- /Doc/7-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Airwhf/MEIAT-CMAQ/HEAD/Doc/7-2.png -------------------------------------------------------------------------------- /Doc/7-3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Airwhf/MEIAT-CMAQ/HEAD/Doc/7-3.png 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-------------------------------------------------------------------------------- 1 | vglvltop,power,industry 2 | 1,0,0.06 3 | 0.995,0,0.16 4 | 0.99,0.0025,0.75 5 | 0.985,0.51,0.03 6 | 0.98,0.453,0 7 | 0.975,0.0325,0 8 | 0.97,0.002,0 9 | -------------------------------------------------------------------------------- /shapefile/MEIC-0P25.prj: -------------------------------------------------------------------------------- 1 | GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]] -------------------------------------------------------------------------------- /input/GRIDDESC.CN27km: -------------------------------------------------------------------------------- 1 | ' ' 2 | 'LamCon_34N_110E' 3 | 2 25.000 40.000 102.000 102.000 34.000 4 | ' ' 5 | 'CN27km' 6 | 'LamCon_34N_110E' -2511000.000 -1775000.000 27000.000 27000.000 186 156 1 7 | ' ' 8 | -------------------------------------------------------------------------------- /input/GRIDDESC.PRD274x181: -------------------------------------------------------------------------------- 1 | ' ' 2 | 'LamCon_40N_97W' 3 | 2 25.000 40.000 110.000 110.000 28.500 4 | ' ' 5 | 'PRD274x181' 6 | 'LamCon_40N_97W' 48000.000 -902500.000 3000.000 3000.000 271 178 1 7 | ' ' 8 | -------------------------------------------------------------------------------- /temporal/weekly.csv: -------------------------------------------------------------------------------- 1 | weekly,power,industry,residential,transportation,agriculture 2 | 0,0.13,0.078,0.143,0.108,0.143 3 | 1,0.147,0.162,0.143,0.155,0.143 4 | 2,0.147,0.162,0.143,0.155,0.143 5 | 3,0.147,0.162,0.143,0.155,0.143 6 | 4,0.147,0.162,0.143,0.155,0.143 7 | 5,0.147,0.162,0.143,0.155,0.143 8 | 6,0.135,0.112,0.143,0.117,0.143 9 | -------------------------------------------------------------------------------- /coarse_emission_2_fine_emission.py: -------------------------------------------------------------------------------- 1 | from src import * 2 | 3 | os.environ["IOAPI_ISPH"] = "6370000." 4 | 5 | 6 | if __name__ == '__main__': 7 | start_time = time.time() 8 | main_coarse2fine() 9 | end_time = time.time() 10 | elapsed_time = end_time - start_time 11 | print(f"### Time consuming: {elapsed_time} s ###") 12 | -------------------------------------------------------------------------------- /UTIL/rename_original_inventory/README.md: -------------------------------------------------------------------------------- 1 | # 重命名原始排放清单文件名 2 | 3 | * 修改输入参数 4 | ```python 5 | tbl_name = {"org": ["PMcoarse"], 6 | "new": ["PMC"]} 7 | 8 | input_dir = r"H:\MEIC\GeoTiff-2017" 9 | output_dir = r"H:\MEIC\GeoTiff-2017_rename" 10 | ``` 11 | * 运行代码 12 | ```shell 13 | cd UTIL/rename_original_inventory 14 | python ./rename_original_inventory_(pollutant).py 15 | ``` 16 | 17 | -------------------------------------------------------------------------------- /shapefile/CN-Province.prj: -------------------------------------------------------------------------------- 1 | PROJCS["China_Lambert_Conformal_Conic",GEOGCS["GCS_Beijing_1954",DATUM["D_Beijing_1954",SPHEROID["Krasovsky_1940",6378245.0,298.3]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",105.0],PARAMETER["Standard_Parallel_1",30.0],PARAMETER["Standard_Parallel_2",62.0],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] -------------------------------------------------------------------------------- /Create-CMAQ-Emission-File.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/3/15 16:34 4 | # @Author :Haofan Wang 5 | # @Email :wanghf58@mail2.sysu.edu.cn 6 | 7 | from src import * 8 | 9 | os.environ["IOAPI_ISPH"] = "6370000." 10 | 11 | 12 | if __name__ == "__main__": 13 | start_time = time.time() 14 | main_createCMAQ() 15 | end_time = time.time() 16 | elapsed_time = end_time - start_time 17 | print(f"### Time consuming: {elapsed_time} s ###") 18 | -------------------------------------------------------------------------------- /temporal/monthly.csv: -------------------------------------------------------------------------------- 1 | monthly,power,industry,residential,transportation,agriculture 2 | 1,0.115,0.0833,0.0833,0.0833,0.12 3 | 2,0.115,0.0833,0.0833,0.0833,0.1 4 | 3,0.1,0.0833,0.0833,0.0833,0.05 5 | 4,0.095,0.0833,0.0833,0.0833,0.01 6 | 5,0.08,0.0833,0.0833,0.0833,0.15 7 | 6,0.05,0.0833,0.0833,0.0833,0.15 8 | 7,0.05,0.0833,0.0833,0.0833,0.05 9 | 8,0.05,0.0833,0.0833,0.0833,0.01 10 | 9,0.05,0.0833,0.0833,0.0833,0.01 11 | 10,0.09,0.0833,0.0833,0.0833,0.05 12 | 11,0.095,0.0833,0.0833,0.0833,0.15 13 | 12,0.11,0.0833,0.0833,0.0833,0.15 14 | -------------------------------------------------------------------------------- /UTIL/rename_original_inventory/rename_original_inventory_(pollutant).py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/5/11 15:15 4 | # @Author :Haofan Wang 5 | # @Email :wanghf58@mail2.sysu.edu.cn 6 | 7 | from src import * 8 | 9 | if __name__ == "__main__": 10 | 11 | tbl_name = {"org": ["PMcoarse"], 12 | "new": ["PMC"]} 13 | 14 | input_dir = r"H:\MEIC\GeoTiff-2017" 15 | output_dir = r"H:\MEIC\GeoTiff-2017_rename" 16 | 17 | main_rename_original_pollutant(tbl_name, input_dir, output_dir) 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /fine_emission_2_coarse_emission.py: -------------------------------------------------------------------------------- 1 | # @Time : 2023/02/21 19:17 2 | # @Author : Haofan Wang 3 | # @Version : python3.9 4 | # @Email : wanghf58@mail2.sysu.edu.cn 5 | import os 6 | 7 | from src import * 8 | 9 | os.environ["IOAPI_ISPH"] = "6370000." 10 | # Ignore the warning information from pandas. 11 | pd.options.mode.chained_assignment = None 12 | 13 | 14 | if __name__ == "__main__": 15 | start_time = time.time() 16 | main_f2c() 17 | end_time = time.time() 18 | elapsed_time = end_time - start_time 19 | print(f"### Time consuming: {elapsed_time} s ###") 20 | -------------------------------------------------------------------------------- /vertical_allocation.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2022/11/22 16:03 4 | # @Author :Haofan Wang 5 | 6 | from src import * 7 | 8 | if __name__ == "__main__": 9 | sectors = ['power', 'industry'] 10 | 11 | start_time = time.time() 12 | for sector in sectors: 13 | files = glob.glob(fr"output\*_{sector}_*.nc") 14 | main_vertical_allocation(files) 15 | 16 | end_time = time.time() 17 | elapsed_time = end_time - start_time 18 | print(f"### Time consuming: {elapsed_time} s ###") 19 | 20 | 21 | 22 | 23 | 24 | -------------------------------------------------------------------------------- /Doc/how_to_treat_the_emssion_which_resolution_is_fine_cn.md: -------------------------------------------------------------------------------- 1 | # 7.原始清单分辨率如果小于模拟域网格分辨率应该如何处理？ 2 | 3 | ------------------------ 4 | 5 | **作者：邱嘉馨** 6 | 7 | ------------------------ 8 | 9 | 1.首先，修改namelist.input，设置好网格信息文件GRIDDESC以及网格名等变量，以及时间分配系数文件和物种分配系数文件等，具体可参考步骤一。 10 | 11 | ![7-1.png](7-1.png) 12 | 13 | 2.打开终端，运行将精细化清单处理到粗网格程序python ./fine_emission_2_coarse_emission.py 14 | 15 | ![7-2.png](7-2.png) 16 | 17 | 如上图所示，提示找不到PMC，检查清单文件发现只有PM25和PM10，使用程序calculate-pmc.py计算生成PMC文件即可解决。 18 | 19 | ![7-3.png](7-3.png) 20 | 21 | ![7-4.png](7-4.png) 22 | 23 | 3.重新运行步骤2 ，出现如下界面即运行结束。在output文件夹下生成清单文件。 24 | 25 | ![7-5.png](7-5.png) 26 | 27 | ![7-6.png](7-6.png) -------------------------------------------------------------------------------- /allocator/README.md: -------------------------------------------------------------------------------- 1 | # Allocator 2 | 3 | 这里是一个用于存放空间分配因子的目录。 4 | 5 | 在运行[coarse_emission_2_fine_emission.py](../coarse_emission_2_fine_emission.py)以前，你需要下载这些数据用于空间分配。 6 | 7 | # Download 8 | 9 | **您可以到以下链接中选择性下载您所需要的数据。** 10 | 链接：https://pan.baidu.com/s/1g2-YYE3uuw24iWw_82dedA?pwd=whff 11 | 12 | 1. 土地利用类型数据 13 | * 农田：agriculture.zip 14 | * 工矿用地：industry.zip or power.zip 15 | 16 | 2. 人口密度数据 17 | * 2017年人口密度数据集：landscan-global-2017_nodata.zip 18 | * 2018年人口密度数据集：landscan-global-2018_nodata.zip 19 | 20 | 3. 道路数据 21 | * 高速路：motorway.zip 22 | * 主干道：primary.zip 23 | * 二级干道：secondary.zip 24 | * 社区道路：residential.zip 25 | * 道路数据栅格化：roads-nodata-0.zip 26 | 27 | 4. 平均分配数据 28 | * 平均分配数据：ave-nodata-0.zip 29 | -------------------------------------------------------------------------------- /Doc/how_to_do_vertical_allocation_cn.md: -------------------------------------------------------------------------------- 1 | # 如何对排放文件进行垂直分配？ 2 | 3 | -------------------- 4 | 5 | **作者：王浩帆** 6 | 7 | -------------------- 8 | 9 | 面源排放的垂直分配过程是使用[vertical_allocation.py](../vertical_allocation.py) 10 | 11 | * 程序提供了power和industry两个部门的垂直分配方案，分别是`profile-industry.csv`和`profile-power.csv`，用户也可以按照已提供的两个文件格式自定义垂直分配系数。 12 | 13 | 由于[vertical_allocation.py](../vertical_allocation.py)只能够识别文件名为`profile.csv`的文件，所以在进行分配时，需要将文件复制为`profile.csv`。 14 | 15 | 以下步骤通过对power部门的排放来进行说明： 16 | 17 | 1. 复制`profile-power.csv`到`profile.csv`。 18 | 19 | 2. 打开[vertical_allocation.py](../vertical_allocation.py)修改以下代码： 20 | 21 | ```python 22 | files = glob.glob(r"output\*_power_*.nc") 23 | ``` 24 | 25 | 3. 在终端输入以下命令即可开始运行。 26 | 27 | ```shell 28 | python .\vertical_allocation.py 29 | ``` 30 | 31 | 4. 在`output/vertical`路径下可以看到垂直分配后的结果。 32 | -------------------------------------------------------------------------------- /Create_CMAQ_mask.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/5/11 13:27 4 | # @Author :Haofan Wang 5 | # @Email :wanghf58@mail2.sysu.edu.cn 6 | 7 | 8 | from src import * 9 | 10 | os.environ['IOAPI_ISPH'] = '6370000.' 11 | 12 | if __name__ == "__main__": 13 | # ------------------------------------------------------------ 14 | # Shapefile path. 15 | shapefile_path = "shapefile/Anyang-around" 16 | # The field of each area. 17 | field = "NAME" 18 | # The output path. 19 | output_name = "output/mask.nc" 20 | # ------------------------------------------------------------ 21 | 22 | start_time = time.time() 23 | main_create_CMAQ_mask(shapefile_path, field, output_name) 24 | end_time = time.time() 25 | elapsed_time = end_time - start_time 26 | print(f"### Time consuming: {elapsed_time} s ###") 27 | 28 | 29 | -------------------------------------------------------------------------------- /Doc/how_to_use_shapefile_for_mask_cn.md: -------------------------------------------------------------------------------- 1 | # 如何使用shapefile文件生成CMAQ中的mask文件？ 2 | 3 | ------------------------ 4 | 5 | **作者：邱嘉馨** 6 | 7 | ------------------------ 8 | 9 | ## 1. 准备shapefile文件 10 | 11 | shapefile文件主要有以下几点要求： 12 | 13 | * 投影必须为WGS1984投影。 14 | * 属性表中不能有中文字符。 15 | * 属性表中必须有字符串字段被程序提取。 16 | * 该字符串字段中的必须为大写字母。 17 | 18 | 具体格式可以参考：[Anyang-around.shp](../shapefile/Anyang-around.shp) 19 | 20 | ## 2. 配置好文件 21 | 22 | 除了上述需要在代码中修改的部分以外， `GRIDDESC`文件也是程序所必须的，但是此文件将从namelist.input中去获取。 23 | 24 | ```python 25 | # ------------------------------------------------------------ 26 | # Shapefile path. 27 | shapefile_path = "shapefile文件路径" 28 | # The field of each area. 29 | field = "字符串字段名称" 30 | # The output path. 31 | output_name = "输出文件名称" 32 | # ------------------------------------------------------------ 33 | ``` 34 | 35 | ## 3. 运行程序 36 | 37 | 在终端中输入： 38 | 39 | ```shell 40 | python ./Create_CMAQ_mask.py 41 | ``` 42 | 43 | 即可运行，并输出文件。 44 | 45 | 46 | -------------------------------------------------------------------------------- /temporal/hourly.csv: -------------------------------------------------------------------------------- 1 | hourly,power,industry,residential,transportation,agriculture 2 | 0,0.032,0.026,0.038,0.017,0.026 3 | 1,0.03,0.007,0.038,0.013,0.019 4 | 2,0.029,0.007,0.03,0.014,0.019 5 | 3,0.028,0.007,0.045,0.015,0.018 6 | 4,0.029,0.007,0.045,0.016,0.019 7 | 5,0.032,0.007,0.038,0.016,0.021 8 | 6,0.035,0.007,0.03,0.029,0.029 9 | 7,0.04,0.029,0.03,0.056,0.033 10 | 8,0.0433,0.045,0.038,0.0599,0.0473 11 | 9,0.0457,0.068,0.038,0.059,0.0576 12 | 10,0.0479,0.068,0.03,0.0594,0.07 13 | 11,0.0495,0.068,0.045,0.0501,0.0885 14 | 12,0.0495,0.068,0.045,0.0501,0.0885 15 | 13,0.0497,0.068,0.038,0.0588,0.0823 16 | 14,0.0501,0.068,0.03,0.06,0.0803 17 | 15,0.05,0.068,0.03,0.062,0.07 18 | 16,0.0497,0.068,0.038,0.0594,0.0597 19 | 17,0.0489,0.066,0.075,0.0574,0.0453 20 | 18,0.0477,0.063,0.075,0.0557,0.0309 21 | 19,0.0473,0.037,0.075,0.049,0.0268 22 | 20,0.0466,0.037,0.075,0.0454,0.0226 23 | 21,0.044,0.037,0.054,0.0417,0.0206 24 | 22,0.0397,0.037,0.018,0.0308,0.0206 25 | 23,0.0352,0.037,0.018,0.0216,0.0206 26 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea 2 | __pycache__/ 3 | allocator/*.tif 4 | allocator/*.zip 5 | output/ 6 | allocator/motorway.cpg 7 | allocator/motorway.dbf 8 | allocator/motorway.prj 9 | allocator/motorway.sbn 10 | allocator/motorway.sbx 11 | allocator/motorway.shp 12 | allocator/motorway.shx 13 | allocator/primary.cpg 14 | allocator/primary.dbf 15 | allocator/primary.prj 16 | allocator/primary.sbn 17 | allocator/primary.sbx 18 | allocator/primary.shp 19 | allocator/primary.shx 20 | allocator/residential.cpg 21 | allocator/residential.dbf 22 | allocator/residential.prj 23 | allocator/residential.sbn 24 | allocator/residential.sbx 25 | allocator/residential.shp 26 | allocator/residential.shx 27 | allocator/secondary.cpg 28 | allocator/secondary.dbf 29 | allocator/secondary.prj 30 | allocator/secondary.sbn 31 | allocator/secondary.sbx 32 | allocator/secondary.shp 33 | allocator/secondary.shx 34 | comparison/emission_transportation_20220102.nc 35 | build/ 36 | src.c 37 | test.py 38 | input/GRIDDESC.CN245x195 39 | mask.nc 40 | -------------------------------------------------------------------------------- /namelist.input: -------------------------------------------------------------------------------- 1 | &global 2 | griddesc_file = "input/GRIDDESC.PRD274x181" 3 | griddesc_name = "PRD274x181" 4 | big_grid_file = "shapefile/MEIC-0P25.shp" 5 | geotiff_dir = "H:/MEIC/GeoTiff-2017" 6 | inventory_label = "MEIC" 7 | inventory_year = "2017" 8 | sectors = 'transportation', 'residential', 'power', 'agriculture', 'industry' 9 | allocator = 'line', 'landscan-global-2017_nodata.tif', 'power.tif', 'agriculture.tif', 'industry.tif', 10 | allocator_type = "line", "raster", "raster", "raster", "raster" 11 | inventory_mechanism = "MEIC-CB05" 12 | target_mechanism = "CB06" 13 | start_date = "2020-07-01" 14 | end_date = "2020-07-02" 15 | cores = 4 16 | / 17 | 18 | &line 19 | line_files = "motorway.shp", "primary.shp", "residential.shp", "secondary.shp" 20 | line_factors = 0.435798, 0.326848, 0.081712, 0.155642, 21 | / 22 | 23 | &control 24 | create_grid = 1, 25 | grid_info = 1, 26 | create_factor = 1, 27 | coarse_emission = 1, 28 | create_source = 1, 29 | / -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | MEIAT-CMAQ v1.4-OA User's Guide 2 | -------------------------------- 3 | |doi| 4 | 5 | .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8001532.svg 6 | :target: https://doi.org/10.5281/zenodo.8001532 7 | 8 | MEIAT-CMAQ: 一款针对CMAQ模型的排放清单分配工具。 9 | 10 | 作者: `王浩帆 `_，邱嘉馨 11 | 12 | **联系作者** 13 | 14 | Email: wanghf58@mail2.sysu.edu.cn 15 | 16 | QQ群: 805594259 17 | 18 | 微信公众号：小王的科研笔记 19 | 20 | 如何引用（这真的很重要！！！） 21 | ============================ 22 | 23 | **非常重要**：如果大家使用或者参考了本工具进行了一些工作，请大家引用下面的文章，这对我真的非常重要，谢谢！！！ 24 | 25 | **Wang, H.**, Qiu, J., Liu, Y., Fan, Q., Lu, X., Zhang, Y., Wu, K., ..., & Sun, J., Wang, H., (2024). MEIAT-CMAQ: A Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model. Atmospheric Environment, 2024, 120614. 26 | 27 | url: https://doi.org/10.1016/j.atmosenv.2024.120604 28 | 29 | 30 | 教程合集 31 | ======= 32 | 33 | `点击此处进行查看。 `_ 34 | 35 | 36 | 反馈问题 37 | ======= 38 | 39 | 如果在您的使用过程中遇到了任何问题，推荐通过`此网页 `_进行反馈，我会及时回复。 40 | 41 | -------------------------------------------------------------------------------- /Doc/how_to_treat_the_yearly_emission_cn.md: -------------------------------------------------------------------------------- 1 | # 如何处理只有年总量的排放清单？ 2 | 3 | ------------------------ 4 | 5 | **作者：邱嘉馨** 6 | 7 | ------------------------ 8 | 9 | 本教程讲解如何使用年总量的排放清单。 10 | 11 | 1. 首先，使用[year2month.py](../year2month.py)将meic年清单的asc格式转为tiff（注意检查输出文件名的格式，部门之间是两个下划线）。 12 | 13 | ```python 14 | input_dir = r"MEIC清单asc格式文件存放位置" 15 | output_dir = r"MEIC清单tiff格式文件的输出位置" 16 | ``` 17 | 配置好代码以后，在终端中输入命令： 18 | 19 | ```shell 20 | python ./PREP/meic_2_GeoTiff.py 21 | ``` 22 | 23 | 运行成功以后将会在输出路径下看到GeoTiff格式文件，如图所示： 24 | ![meic2geotiff_output.png](meic2geotiff_output.png) 25 | 26 | 2. 使用[year2month.py](../year2month.py)将年清单转换为月清单。 27 | 28 | 设置文件读取和输出路径： 29 | 30 | ```python 31 | input_dir = r"年清单的tiff格式文件的存放路径" 32 | output_dir = r"月清单的输出路径" 33 | ``` 34 | 35 | 设置月分配系数： 36 | 37 | 月时间分配系数文件为[monthly.csv](../temporal/monthly.csv)，可以通过修改此文件设置不同部门的 38 | 月分配系数，注意列标题和清单文件名中的部门拼写保持一致。 39 | 40 | ![monthly_profile.png](monthly_profile.png) 41 | 42 | 如上配置好代码和时间分配系数表以后，在终端中输入以下命令并运行即可。 43 | 44 | ```shell 45 | python ./year2month.py 46 | ``` 47 | 48 | 运行成功以后，将会在输出路径下看到以下逐月的清单文件： 49 | ![year2month_output.png](year2month_output.png) 50 | 51 | 3. 后续步骤见表1。 -------------------------------------------------------------------------------- /species/MEIC-CB05_CB06_speciate_agriculture.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | CB05_ALD2,ALD2,1,44.03499,Mmol,mol/s 3 | CB05_ALDX,ALDX,1,39.22808,Mmol,mol/s 4 | CB05_ETH,ETH,1,28.053,Mmol,mol/s 5 | CB05_ETOH,ETOH,1,30.069,Mmol,mol/s 6 | CB05_FORM,FORM,1,45.8447,Mmol,mol/s 7 | CB05_IOLE,IOLE,1,30.026,Mmol,mol/s 8 | CB05_ISOP,ISOP,1,57.09965,Mmol,mol/s 9 | CB05_MEOH,MEOH,1,68.117,Mmol,mol/s 10 | CB05_OLE,OLE,1,32.042,Mmol,mol/s 11 | CB05_PAR,PAR,1,0.992943,Mmol,mol/s 12 | CB05_TERP,TERP,1,29.63422,Mmol,mol/s 13 | CB05_TOL,TOL,1,15.11587,Mmol,mol/s 14 | CB05_UNR,UNR,1,136.2352,Mmol,mol/s 15 | CB05_XYL,XYL,1,96.79334,Mmol,mol/s 16 | PM25,PAL,1,0,Mg,g/s 17 | PM25,PCA,1,0,Mg,g/s 18 | PM25,PCL,1,0,Mg,g/s 19 | PM25,PFE,1,0,Mg,g/s 20 | PM25,PH2O,1,0,Mg,g/s 21 | PM25,PK,1,0,Mg,g/s 22 | PM25,PMG,1,0,Mg,g/s 23 | PM25,PMN,1,0,Mg,g/s 24 | PM25,PMOTHR,1,0,Mg,g/s 25 | PM25,PNA,1,0,Mg,g/s 26 | PM25,PNCOM,1,0,Mg,g/s 27 | PM25,PNH4,1,0,Mg,g/s 28 | PM25,PNO3,1,0,Mg,g/s 29 | PM25,PSI,1,0,Mg,g/s 30 | PM25,PSO4,1,0,Mg,g/s 31 | PM25,PTI,1,0,Mg,g/s 32 | SO2,SO2,0,64,Mg,mol/s 33 | SO2,SULF,0,98,Mg,mol/s 34 | NH3,NH3,1,17,Mg,mol/s 35 | CO,CO,0,28,Mg,mol/s 36 | PMC,PMC,0,1,Mg,g/s 37 | NOx,NO,0,30,Mg,mol/s 38 | NOx,NO2,0,46,Mg,mol/s 39 | NOx,HONO,0,47,Mg,mol/s 40 | OC,POC,0,1,Mg,g/s 41 | -------------------------------------------------------------------------------- /species/MEIC-CB05_CB06_speciate_industry.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | CB05_ALD2,ALD2,1,44.03499,Mmol,mol/s 3 | CB05_ALDX,ALDX,1,39.22808,Mmol,mol/s 4 | CB05_ETH,ETH,1,28.053,Mmol,mol/s 5 | CB05_ETOH,ETOH,1,30.069,Mmol,mol/s 6 | CB05_FORM,FORM,1,45.8447,Mmol,mol/s 7 | CB05_IOLE,IOLE,1,30.026,Mmol,mol/s 8 | CB05_ISOP,ISOP,1,57.09965,Mmol,mol/s 9 | CB05_MEOH,MEOH,1,68.117,Mmol,mol/s 10 | CB05_OLE,OLE,1,32.042,Mmol,mol/s 11 | CB05_PAR,PAR,1,0.992943,Mmol,mol/s 12 | CB05_TERP,TERP,1,29.63422,Mmol,mol/s 13 | CB05_TOL,TOL,1,15.11587,Mmol,mol/s 14 | CB05_UNR,UNR,1,136.2352,Mmol,mol/s 15 | CB05_XYL,XYL,1,96.79334,Mmol,mol/s 16 | PM25,PAL,0.030838,1,Mg,g/s 17 | PM25,PCA,0.04692,1,Mg,g/s 18 | PM25,PCL,0.005623,1,Mg,g/s 19 | PM25,PFE,0.029831,1,Mg,g/s 20 | PM25,PH2O,0.007782,1,Mg,g/s 21 | PM25,PK,0.020635,1,Mg,g/s 22 | PM25,PMG,0.001488,1,Mg,g/s 23 | PM25,PMN,0.001368,1,Mg,g/s 24 | PM25,PMOTHR,0.569705,1,Mg,g/s 25 | PM25,PNA,0.01045,1,Mg,g/s 26 | PM25,PNCOM,0.078912,1,Mg,g/s 27 | PM25,PNH4,0.001819,1,Mg,g/s 28 | PM25,PNO3,0.004444,1,Mg,g/s 29 | PM25,PSI,0.089135,1,Mg,g/s 30 | PM25,PSO4,0.096941,1,Mg,g/s 31 | PM25,PTI,0.00411,1,Mg,g/s 32 | SO2,SO2,1,64,Mg,mol/s 33 | SO2,SULF,0.00919,98,Mg,mol/s 34 | NH3,NH3,1,17,Mg,mol/s 35 | CO,CO,1,28,Mg,mol/s 36 | PMC,PMC,1,1,Mg,g/s 37 | NOx,NO,0.9,30,Mg,mol/s 38 | NOx,NO2,0.092,46,Mg,mol/s 39 | NOx,HONO,0.008,47,Mg,mol/s 40 | OC,POC,1,1,Mg,g/s 41 | -------------------------------------------------------------------------------- /species/MEIC-CB05_CB06_speciate_power.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | CB05_ALD2,ALD2,1,44.03499,Mmol,mol/s 3 | CB05_ALDX,ALDX,1,39.22808,Mmol,mol/s 4 | CB05_ETH,ETH,1,28.053,Mmol,mol/s 5 | CB05_ETOH,ETOH,1,30.069,Mmol,mol/s 6 | CB05_FORM,FORM,1,45.8447,Mmol,mol/s 7 | CB05_IOLE,IOLE,1,30.026,Mmol,mol/s 8 | CB05_ISOP,ISOP,1,57.09965,Mmol,mol/s 9 | CB05_MEOH,MEOH,1,68.117,Mmol,mol/s 10 | CB05_OLE,OLE,1,32.042,Mmol,mol/s 11 | CB05_PAR,PAR,1,0.992943,Mmol,mol/s 12 | CB05_TERP,TERP,1,29.63422,Mmol,mol/s 13 | CB05_TOL,TOL,1,15.11587,Mmol,mol/s 14 | CB05_UNR,UNR,1,136.2352,Mmol,mol/s 15 | CB05_XYL,XYL,1,96.79334,Mmol,mol/s 16 | PM25,PAL,0.050235,1,Mg,g/s 17 | PM25,PCA,0.034628,1,Mg,g/s 18 | PM25,PCL,0.001152,1,Mg,g/s 19 | PM25,PFE,0.025104,1,Mg,g/s 20 | PM25,PH2O,3.95E-05,1,Mg,g/s 21 | PM25,PK,0.004844,1,Mg,g/s 22 | PM25,PMG,0.000459,1,Mg,g/s 23 | PM25,PMN,0.000248,1,Mg,g/s 24 | PM25,PMOTHR,0.588929,1,Mg,g/s 25 | PM25,PNA,0.000116,1,Mg,g/s 26 | PM25,PNCOM,0.043903,1,Mg,g/s 27 | PM25,PNH4,0.003699,1,Mg,g/s 28 | PM25,PNO3,0.006201,1,Mg,g/s 29 | PM25,PSI,0.0774444,1,Mg,g/s 30 | PM25,PSO4,0.159122,1,Mg,g/s 31 | PM25,PTI,0.003877,1,Mg,g/s 32 | SO2,SO2,1,64,Mg,mol/s 33 | SO2,SULF,0.0218,98,Mg,mol/s 34 | NH3,NH3,1,17,Mg,mol/s 35 | CO,CO,1,28,Mg,mol/s 36 | PMC,PMC,1,1,Mg,g/s 37 | NOx,NO,0.9,30,Mg,mol/s 38 | NOx,NO2,0.092,46,Mg,mol/s 39 | NOx,HONO,0.008,47,Mg,mol/s 40 | OC,POC,1,1,Mg,g/s 41 | -------------------------------------------------------------------------------- /species/MEIC-CB05_CB06_speciate_residential.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | CB05_ALD2,ALD2,1,44.03499,Mmol,mol/s 3 | CB05_ALDX,ALDX,1,39.22808,Mmol,mol/s 4 | CB05_ETH,ETH,1,28.053,Mmol,mol/s 5 | CB05_ETOH,ETOH,1,30.069,Mmol,mol/s 6 | CB05_FORM,FORM,1,45.8447,Mmol,mol/s 7 | CB05_IOLE,IOLE,1,30.026,Mmol,mol/s 8 | CB05_ISOP,ISOP,1,57.09965,Mmol,mol/s 9 | CB05_MEOH,MEOH,1,68.117,Mmol,mol/s 10 | CB05_OLE,OLE,1,32.042,Mmol,mol/s 11 | CB05_PAR,PAR,1,0.992943,Mmol,mol/s 12 | CB05_TERP,TERP,1,29.63422,Mmol,mol/s 13 | CB05_TOL,TOL,1,15.11587,Mmol,mol/s 14 | CB05_UNR,UNR,1,136.2352,Mmol,mol/s 15 | CB05_XYL,XYL,1,96.79334,Mmol,mol/s 16 | PM25,PAL,9.06E-04,1,Mg,g/s 17 | PM25,PCA,0.00343,1,Mg,g/s 18 | PM25,PCL,0.026758,1,Mg,g/s 19 | PM25,PFE,6.75E-04,1,Mg,g/s 20 | PM25,PH2O,2.86E-05,1,Mg,g/s 21 | PM25,PK,0.037723,1,Mg,g/s 22 | PM25,PMG,4.89E-04,1,Mg,g/s 23 | PM25,PMN,2.48E-05,1,Mg,g/s 24 | PM25,PMOTHR,0.075385,1,Mg,g/s 25 | PM25,PNA,5.56E-03,1,Mg,g/s 26 | PM25,PNCOM,0.793224,1,Mg,g/s 27 | PM25,PNH4,0.006773,1,Mg,g/s 28 | PM25,PNO3,0.006223,1,Mg,g/s 29 | PM25,PSI,0.015033,1,Mg,g/s 30 | PM25,PSO4,0.027662,1,Mg,g/s 31 | PM25,PTI,1.03E-04,1,Mg,g/s 32 | SO2,SO2,1,64,Mg,mol/s 33 | SO2,SULF,0.0148,98,Mg,mol/s 34 | NH3,NH3,1,17,Mg,mol/s 35 | CO,CO,1,28,Mg,mol/s 36 | PMC,PMC,1,1,Mg,g/s 37 | NOx,NO,0.9,30,Mg,mol/s 38 | NOx,NO2,0.092,46,Mg,mol/s 39 | NOx,HONO,0.008,47,Mg,mol/s 40 | OC,POC,1,1,Mg,g/s 41 | -------------------------------------------------------------------------------- /species/MEIC-CB05_CB06_speciate_transportation.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | CB05_ALD2,ALD2,1,44.03499,Mmol,mol/s 3 | CB05_ALDX,ALDX,1,39.22808,Mmol,mol/s 4 | CB05_ETH,ETH,1,28.053,Mmol,mol/s 5 | CB05_ETOH,ETOH,1,30.069,Mmol,mol/s 6 | CB05_FORM,FORM,1,45.8447,Mmol,mol/s 7 | CB05_IOLE,IOLE,1,30.026,Mmol,mol/s 8 | CB05_ISOP,ISOP,1,57.09965,Mmol,mol/s 9 | CB05_MEOH,MEOH,1,68.117,Mmol,mol/s 10 | CB05_OLE,OLE,1,32.042,Mmol,mol/s 11 | CB05_PAR,PAR,1,0.992943,Mmol,mol/s 12 | CB05_TERP,TERP,1,29.63422,Mmol,mol/s 13 | CB05_TOL,TOL,1,15.11587,Mmol,mol/s 14 | CB05_UNR,UNR,1,136.2352,Mmol,mol/s 15 | CB05_XYL,XYL,1,96.79334,Mmol,mol/s 16 | PM25,PAL,0.005602,1,Mg,g/s 17 | PM25,PCA,0.033303,1,Mg,g/s 18 | PM25,PCL,0.009179,1,Mg,g/s 19 | PM25,PFE,0.06222,1,Mg,g/s 20 | PM25,PH2O,0.003706,1,Mg,g/s 21 | PM25,PK,0.002276,1,Mg,g/s 22 | PM25,PMG,0.040729,1,Mg,g/s 23 | PM25,PMN,5.00E-04,1,Mg,g/s 24 | PM25,PMOTHR,0.264368,1,Mg,g/s 25 | PM25,PNA,0.002986,1,Mg,g/s 26 | PM25,PNCOM,0.375107,1,Mg,g/s 27 | PM25,PNH4,0.041369,1,Mg,g/s 28 | PM25,PNO3,0.016703,1,Mg,g/s 29 | PM25,PSI,0.043676,1,Mg,g/s 30 | PM25,PSO4,0.09631,1,Mg,g/s 31 | PM25,PTI,0.001967,1,Mg,g/s 32 | SO2,SO2,1,64,Mg,mol/s 33 | SO2,SULF,0,98,Mg,mol/s 34 | NH3,NH3,1,17,Mg,mol/s 35 | CO,CO,1,28,Mg,mol/s 36 | PMC,PMC,1,1,Mg,g/s 37 | NOx,NO,0.9,30,Mg,mol/s 38 | NOx,NO2,0.092,46,Mg,mol/s 39 | NOx,HONO,0.008,47,Mg,mol/s 40 | OC,POC,1,1,Mg,g/s 41 | -------------------------------------------------------------------------------- /README.CN.md: -------------------------------------------------------------------------------- 1 | # 教程目录 2 | 3 | -------------- 4 | 5 | 1. [配置MEIAT-CMAQ的运行环境。](https://mp.weixin.qq.com/s/1CEcoSDeCF9l-an_GD_EJg) 6 | 7 | * 此教程将详细讲解如何配置MEIAT-CMAQ的运行环境。 8 | 9 | -------------- 10 | 11 | 2. [通过MEICv1.3的2017年排放清单制作珠江三角洲模拟域的CMAQ排放文件。](Doc/adopt_meic_for_prd_emission_file_cn.md) 12 | 13 | * 此教程会帮助用户了解最基础的CMAQ排放文件制作流程。 14 | 15 | -------------- 16 | 17 | 3. [跳过空间分配步骤直接输出CMAQ排放文件。](Doc/how_to_treat_the_emssion_which_resolution_is_fine_cn.md) 18 | 19 | * 此教程主要是为了弥补**教程2**中的不足，在**教程2**中，我们仅可能将较粗的排放清单降尺度到较细分辨率，而此教程则帮助我们了解如何使用MEIAT-CMAQ将较细分辨率的排放清单应用到较粗分辨率的模拟域中。 20 | 21 | -------------- 22 | 23 | 4. [在MEIC清单中添加东亚地区的排放（MIX）。](Doc/how_to_combine_meic_and_mix_cn.md) 24 | 25 | * 此教程主要为引导用户学会对GeoTIFF文件进行批量镶嵌工作。 26 | 27 | -------------- 28 | 29 | 5. [在MEIC清单中使用本地排放源。](Doc/adopt_local_emission_to_meic_cn.md) 30 | 31 | * 此教程将会引导用户学会如何灵活使用MEIAT-CMAQ使其能够同时使用表格化排放清单和网格化排放清单。 32 | 33 | -------------- 34 | 35 | 6. [对年尺度的排放清单进行处理。](Doc/how_to_treat_the_yearly_emission_cn.md) 36 | 37 | * MEIAT-CMAQ只允许直接处理月尺度的排放清单，但是我们可以通过月时间分配文件将年尺度清单转换到月尺度清单。 38 | 39 | -------------- 40 | 41 | 7. [将排放文件进行垂直分配。](Doc/how_to_do_vertical_allocation_cn.md) 42 | 43 | * 此教程将引导用户学会如何使用MEIAT-CMAQ对排放清单进行垂直分配。 44 | 45 | -------------- 46 | 47 | 8. [生成CMAQ-ISAM可以使用的区域文件。](Doc/how_to_use_shapefile_for_mask_cn.md) 48 | 49 | * 制作区域文件一直是运行CMAQ-ISAM的一个难点，本教程将引导用户学会如何快速完成对区域文件的制作。 50 | 51 | -------------- 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | -------------------------------------------------------------------------------- /Doc/how_to_combine_meic_and_mix_cn.md: -------------------------------------------------------------------------------- 1 | # 如何同时使用MEIC和MIX清单？ 2 | 3 | ------------------------ 4 | 5 | **作者：王浩帆** 6 | 7 | ------------------------ 8 | 9 | MEIC清单仅为中国境内的排放清单，但是在模拟全国污染场的案例中，中国周边国家的排放是不容忽视的，因此需要通过MIX清单来对MEIC进行一个补充。 10 | 11 | 不论是模拟网格分辨率大于等于清单网格分辨率，还是模拟网格分辨率小于清单网格分辨率的情况，同时使用MEIC和MIX清单的关键步骤都是如何将MEIC清单镶嵌到MIX中， 12 | 作为一系列新的GeoTIFF文件来作为[coarse_emission_2_fine_emission.py](../coarse_emission_2_fine_emission.py)和 13 | [fine_emission_2_coarse_emission.py](../fine_emission_2_coarse_emission.py)的输入。 14 | 15 | **因此本部分将重点讲解如何使用工具来完成两个系列GeoTIFF的镶嵌工作。** 16 | 17 | 1. 将MIX清单和MEIC清单都转换为GeoTiff格式。 18 | * 使用[mix_2_GeoTiff.py](../PREP/mix_2_GeoTiff.py)将MIX清单转换为GeoTiff格式。 19 | * 使用[meic_2_GeoTiff.py](../PREP/meic_2_GeoTiff.py)将MEIC清单转换为GeoTiff格式。 20 | 由于MIX清单中没有PMC，因此需要通过[calculate-pmc.py](../calculate-pmc.py)将其计算出来。 21 | 22 | 2. 配置[combine.py](../UTIL/combine/combine.py)中的输入参数。 23 | 24 | * upper_raster_dir：上层GeoTiff所在目录路径。 25 | * bottom_raster_dir：下层GeoTiff所在目录路径。 26 | * output_dir：输出GeoTiff目录路径。 27 | 28 | 29 | * upper_raster_pollutants：上层GeoTiff需要合并的污染物名称。 30 | * bottom_raster_pollutants：下层GeoTiff所对应的污染物名称。 31 | * output_pollutants：一一对应到的输出污染物的名称。 32 | 33 | 34 | * upper_label：上层GeoTiff标签。 35 | * bottom_label：下层GeoTiff标签。 36 | * output_label：输出GeoTiff标签。 37 | 38 | 39 | * upper_raster_template：任意一个上层GeoTiff文件路径。 40 | * bottom_raster_template：任意一个下层GeoTiff文件路径。 41 | 42 | 43 | * upper_resolution：上层GeoTiff的分辨率。 44 | * bottom_resolution：下层GeoTiff的分辨率。 45 | 46 | 47 | * sectors：需要合并的部门。 48 | 49 | 50 | * upper_year：上层GeoTiff的年份。 51 | * bottom_year：下层GeoTiff的年份。 52 | * output_year：定义输出GeoTiff的年份。 53 | 54 | 55 | 3. 运行[combine.py](../UTIL/combine/combine.py) 56 | 57 | 在终端中输入： 58 | ```shell 59 | python ./combine.py 60 | ``` 61 | 便可以开始运行程序，程序结束后将在`output_dir`中产生合并后的系列GeoTiff。 62 | 63 | 4. 进行空间分配、物种分配和时间分配。 64 | 65 | 此步骤和[第一个教程](1-adopt_meic_for_prd_emission_file.md)或第二个教程中的步骤完全相同，不再赘述。 66 | 67 | -------------------------------------------------------------------------------- /PREP/heic_2_GeoTiff.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2022/12/12 20:07 4 | # @Author :Haofan Wang 5 | import glob 6 | import os 7 | 8 | import xarray as xr 9 | 10 | import rasterio as rio 11 | from rasterio.transform import Affine 12 | 13 | if __name__ == "__main__": 14 | # Set where is the origin inventory in. 15 | fdir = r"H:\MEIC\中国高分辨率大气污染物集成清单" 16 | 17 | # Set the name of inventory. 18 | files = glob.glob(f"{fdir}/*.nc") 19 | 20 | for file in files: 21 | ds = xr.open_dataset(file) 22 | lats = ds.coords["lat"].__array__() 23 | lons = ds.coords["lon"].__array__() 24 | lonmin, latmax, lonmax, latmin = lons.min(), lats.max(), lons.max(), lats.min() 25 | num_lon = ds.__getattr__("xllcorner") 26 | num_lat = ds.__getattr__("yllcorner") 27 | res = 0.1 28 | 29 | transform = Affine.translation(lonmin - res / 2, latmin - res / 2) * Affine.scale(res, res) 30 | 31 | # Set sectors. 32 | sectors = ["POWER", "INDUSTRY", "RESIDENTIAL", "TRANSPORTATION", "SOLVENT", "AGRICULTURE", "BIOMASS", "SHIPPING"] 33 | # Obtain the pollutant. 34 | pollutant = os.path.basename(file).split("_")[0] 35 | 36 | for sector in sectors: 37 | for i in range(12): 38 | # convert to tons/year 39 | temp_ds = ds[f"{pollutant}_{sector}"][i, ...] 40 | mm = r"%.2d" % (i + 1) 41 | tiffile = f"HEIC_2017_{mm}__{sector}__{pollutant}.tiff" 42 | # print(ds) 43 | with rio.open(tiffile, 44 | 'w', 45 | driver='GTiff', 46 | height=num_lat, 47 | width=num_lon, 48 | count=1, 49 | dtype=temp_ds.dtype, 50 | crs='+proj=latlong', 51 | transform=transform, ) as dst: 52 | dst.write(temp_ds, 1) 53 | print(f"Finish and output {tiffile}.") 54 | -------------------------------------------------------------------------------- /species/MEIC-SAPRC07_SAPRC07_speciate_power.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | SAPRC07_AACD,AACD,1,60.05,Mmol,mol/s 3 | SAPRC07_ACET,ACET,1,58.08,Mmol,mol/s 4 | SAPRC07_ACYE,ACYE,1,26.04,Mmol,mol/s 5 | SAPRC07_ALK1,ALK1,1,30.07,Mmol,mol/s 6 | SAPRC07_ALK2,ALK2,1,36.73,Mmol,mol/s 7 | SAPRC07_ALK3,ALK3,1,58.61,Mmol,mol/s 8 | SAPRC07_ALK4,ALK4,1,77.6,Mmol,mol/s 9 | SAPRC07_ALK5,ALK5,1,118.89,Mmol,mol/s 10 | SAPRC07_ARO1,ARO1,1,95.16,Mmol,mol/s 11 | SAPRC07_ARO2,ARO2MN,1,118.72,Mmol,mol/s 12 | SAPRC07_BACL,BACL,1,86.09,Mmol,mol/s 13 | SAPRC07_BALD,BALD,1,106.13,Mmol,mol/s 14 | SAPRC07_BENZ,BENZ,1,78.11,Mmol,mol/s 15 | SAPRC07_CH4,CH4,1,16.04,Mmol,mol/s 16 | SAPRC07_CCHO,CCHO,1,44.05,Mmol,mol/s 17 | SAPRC07_CRES,CRES,1,108.14,Mmol,mol/s 18 | SAPRC07_ETHE,ETHE,1,28.05,Mmol,mol/s 19 | SAPRC07_FACD,FACD,1,46.03,Mmol,mol/s 20 | SAPRC07_GLY,GLY,1,58.04,Mmol,mol/s 21 | SAPRC07_HCHO,HCHO,1,30.03,Mmol,mol/s 22 | SAPRC07_IPRD,IPRD,1,100.12,Mmol,mol/s 23 | SAPRC07_ISOP,ISOP,1,68.12,Mmol,mol/s 24 | SAPRC07_MACR,MACR,1,70.09,Mmol,mol/s 25 | SAPRC07_MEK,MEK,1,72.11,Mmol,mol/s 26 | SAPRC07_MEOH,MEOH,1,32.04,Mmol,mol/s 27 | SAPRC07_MGLY,MGLY,1,72.04,Mmol,mol/s 28 | SAPRC07_MVK,MVK,1,70.09,Mmol,mol/s 29 | SAPRC07_OLE1,OLE1,1,72.34,Mmol,mol/s 30 | SAPRC07_OLE2,OLE2,1,75.78,Mmol,mol/s 31 | SAPRC07_PACD,PACD,1,74.08,Mmol,mol/s 32 | SAPRC07_PRD2,PRD2,1,116.16,Mmol,mol/s 33 | SAPRC07_RCHO,RCHO,1,58.08,Mmol,mol/s 34 | SAPRC07_RNO3,RNO3,1,147.18,Mmol,mol/s 35 | SAPRC07_TERP,TERP,1,136.24,Mmol,mol/s 36 | PM25,PAL,5.60E-03,1,Mg,g/s 37 | PM25,PCA,0.033303,1,Mg,g/s 38 | PM25,PCL,0.009179,1,Mg,g/s 39 | PM25,PFE,6.22E-02,1,Mg,g/s 40 | PM25,PH2O,3.71E-03,1,Mg,g/s 41 | PM25,PK,0.002276,1,Mg,g/s 42 | PM25,PMG,4.07E-02,1,Mg,g/s 43 | PM25,PMN,5.00E-04,1,Mg,g/s 44 | PM25,PMOTHR,0.264368,1,Mg,g/s 45 | PM25,PNA,2.99E-03,1,Mg,g/s 46 | PM25,PNCOM,0.375107,1,Mg,g/s 47 | PM25,PNH4,0.041369,1,Mg,g/s 48 | PM25,PNO3,0.016703,1,Mg,g/s 49 | PM25,PSI,0.043676,1,Mg,g/s 50 | PM25,PSO4,0.09631,1,Mg,g/s 51 | PM25,PTI,1.97E-03,1,Mg,g/s 52 | SO2,SO2,1,64,Mg,mol/s 53 | SO2,SULF,0,98,Mg,mol/s 54 | NH3,NH3,1,17,Mg,mol/s 55 | CO,CO,1,28,Mg,mol/s 56 | PMC,PMC,1,1,Mg,g/s 57 | NOx,NO,0.9,30,Mg,mol/s 58 | NOx,NO2,0.092,46,Mg,mol/s 59 | NOx,HONO,0.008,47,Mg,mol/s 60 | OC,POC,1,1,Mg,g/s 61 | -------------------------------------------------------------------------------- /species/MEIC-SAPRC07_SAPRC07_speciate_agriculture.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | SAPRC07_AACD,AACD,1,60.05,Mmol,mol/s 3 | SAPRC07_ACET,ACET,1,58.08,Mmol,mol/s 4 | SAPRC07_ACYE,ACYE,1,26.04,Mmol,mol/s 5 | SAPRC07_ALK1,ALK1,1,30.07,Mmol,mol/s 6 | SAPRC07_ALK2,ALK2,1,36.73,Mmol,mol/s 7 | SAPRC07_ALK3,ALK3,1,58.61,Mmol,mol/s 8 | SAPRC07_ALK4,ALK4,1,77.6,Mmol,mol/s 9 | SAPRC07_ALK5,ALK5,1,118.89,Mmol,mol/s 10 | SAPRC07_ARO1,ARO1,1,95.16,Mmol,mol/s 11 | SAPRC07_ARO2,ARO2MN,1,118.72,Mmol,mol/s 12 | SAPRC07_BACL,BACL,1,86.09,Mmol,mol/s 13 | SAPRC07_BALD,BALD,1,106.13,Mmol,mol/s 14 | SAPRC07_BENZ,BENZ,1,78.11,Mmol,mol/s 15 | SAPRC07_CH4,CH4,1,16.04,Mmol,mol/s 16 | SAPRC07_CCHO,CCHO,1,44.05,Mmol,mol/s 17 | SAPRC07_CRES,CRES,1,108.14,Mmol,mol/s 18 | SAPRC07_ETHE,ETHE,1,28.05,Mmol,mol/s 19 | SAPRC07_FACD,FACD,1,46.03,Mmol,mol/s 20 | SAPRC07_GLY,GLY,1,58.04,Mmol,mol/s 21 | SAPRC07_HCHO,HCHO,1,30.03,Mmol,mol/s 22 | SAPRC07_IPRD,IPRD,1,100.12,Mmol,mol/s 23 | SAPRC07_ISOP,ISOP,1,68.12,Mmol,mol/s 24 | SAPRC07_MACR,MACR,1,70.09,Mmol,mol/s 25 | SAPRC07_MEK,MEK,1,72.11,Mmol,mol/s 26 | SAPRC07_MEOH,MEOH,1,32.04,Mmol,mol/s 27 | SAPRC07_MGLY,MGLY,1,72.04,Mmol,mol/s 28 | SAPRC07_MVK,MVK,1,70.09,Mmol,mol/s 29 | SAPRC07_OLE1,OLE1,1,72.34,Mmol,mol/s 30 | SAPRC07_OLE2,OLE2,1,75.78,Mmol,mol/s 31 | SAPRC07_PACD,PACD,1,74.08,Mmol,mol/s 32 | SAPRC07_PRD2,PRD2,1,116.16,Mmol,mol/s 33 | SAPRC07_RCHO,RCHO,1,58.08,Mmol,mol/s 34 | SAPRC07_RNO3,RNO3,1,147.18,Mmol,mol/s 35 | SAPRC07_TERP,TERP,1,136.24,Mmol,mol/s 36 | PM25,PAL,5.60E-03,1,Mg,g/s 37 | PM25,PCA,0.033303,1,Mg,g/s 38 | PM25,PCL,0.009179,1,Mg,g/s 39 | PM25,PFE,6.22E-02,1,Mg,g/s 40 | PM25,PH2O,3.71E-03,1,Mg,g/s 41 | PM25,PK,0.002276,1,Mg,g/s 42 | PM25,PMG,4.07E-02,1,Mg,g/s 43 | PM25,PMN,5.00E-04,1,Mg,g/s 44 | PM25,PMOTHR,0.264368,1,Mg,g/s 45 | PM25,PNA,2.99E-03,1,Mg,g/s 46 | PM25,PNCOM,0.375107,1,Mg,g/s 47 | PM25,PNH4,0.041369,1,Mg,g/s 48 | PM25,PNO3,0.016703,1,Mg,g/s 49 | PM25,PSI,0.043676,1,Mg,g/s 50 | PM25,PSO4,0.09631,1,Mg,g/s 51 | PM25,PTI,1.97E-03,1,Mg,g/s 52 | SO2,SO2,1,64,Mg,mol/s 53 | SO2,SULF,0,98,Mg,mol/s 54 | NH3,NH3,1,17,Mg,mol/s 55 | CO,CO,1,28,Mg,mol/s 56 | PMC,PMC,1,1,Mg,g/s 57 | NOx,NO,0.9,30,Mg,mol/s 58 | NOx,NO2,0.092,46,Mg,mol/s 59 | NOx,HONO,0.008,47,Mg,mol/s 60 | OC,POC,1,1,Mg,g/s 61 | -------------------------------------------------------------------------------- /species/MEIC-SAPRC07_SAPRC07_speciate_industry.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | SAPRC07_AACD,AACD,1,60.05,Mmol,mol/s 3 | SAPRC07_ACET,ACET,1,58.08,Mmol,mol/s 4 | SAPRC07_ACYE,ACYE,1,26.04,Mmol,mol/s 5 | SAPRC07_ALK1,ALK1,1,30.07,Mmol,mol/s 6 | SAPRC07_ALK2,ALK2,1,36.73,Mmol,mol/s 7 | SAPRC07_ALK3,ALK3,1,58.61,Mmol,mol/s 8 | SAPRC07_ALK4,ALK4,1,77.6,Mmol,mol/s 9 | SAPRC07_ALK5,ALK5,1,118.89,Mmol,mol/s 10 | SAPRC07_ARO1,ARO1,1,95.16,Mmol,mol/s 11 | SAPRC07_ARO2,ARO2MN,1,118.72,Mmol,mol/s 12 | SAPRC07_BACL,BACL,1,86.09,Mmol,mol/s 13 | SAPRC07_BALD,BALD,1,106.13,Mmol,mol/s 14 | SAPRC07_BENZ,BENZ,1,78.11,Mmol,mol/s 15 | SAPRC07_CH4,CH4,1,16.04,Mmol,mol/s 16 | SAPRC07_CCHO,CCHO,1,44.05,Mmol,mol/s 17 | SAPRC07_CRES,CRES,1,108.14,Mmol,mol/s 18 | SAPRC07_ETHE,ETHE,1,28.05,Mmol,mol/s 19 | SAPRC07_FACD,FACD,1,46.03,Mmol,mol/s 20 | SAPRC07_GLY,GLY,1,58.04,Mmol,mol/s 21 | SAPRC07_HCHO,HCHO,1,30.03,Mmol,mol/s 22 | SAPRC07_IPRD,IPRD,1,100.12,Mmol,mol/s 23 | SAPRC07_ISOP,ISOP,1,68.12,Mmol,mol/s 24 | SAPRC07_MACR,MACR,1,70.09,Mmol,mol/s 25 | SAPRC07_MEK,MEK,1,72.11,Mmol,mol/s 26 | SAPRC07_MEOH,MEOH,1,32.04,Mmol,mol/s 27 | SAPRC07_MGLY,MGLY,1,72.04,Mmol,mol/s 28 | SAPRC07_MVK,MVK,1,70.09,Mmol,mol/s 29 | SAPRC07_OLE1,OLE1,1,72.34,Mmol,mol/s 30 | SAPRC07_OLE2,OLE2,1,75.78,Mmol,mol/s 31 | SAPRC07_PACD,PACD,1,74.08,Mmol,mol/s 32 | SAPRC07_PRD2,PRD2,1,116.16,Mmol,mol/s 33 | SAPRC07_RCHO,RCHO,1,58.08,Mmol,mol/s 34 | SAPRC07_RNO3,RNO3,1,147.18,Mmol,mol/s 35 | SAPRC07_TERP,TERP,1,136.24,Mmol,mol/s 36 | PM25,PAL,5.60E-03,1,Mg,g/s 37 | PM25,PCA,0.033303,1,Mg,g/s 38 | PM25,PCL,0.009179,1,Mg,g/s 39 | PM25,PFE,6.22E-02,1,Mg,g/s 40 | PM25,PH2O,3.71E-03,1,Mg,g/s 41 | PM25,PK,0.002276,1,Mg,g/s 42 | PM25,PMG,4.07E-02,1,Mg,g/s 43 | PM25,PMN,5.00E-04,1,Mg,g/s 44 | PM25,PMOTHR,0.264368,1,Mg,g/s 45 | PM25,PNA,2.99E-03,1,Mg,g/s 46 | PM25,PNCOM,0.375107,1,Mg,g/s 47 | PM25,PNH4,0.041369,1,Mg,g/s 48 | PM25,PNO3,0.016703,1,Mg,g/s 49 | PM25,PSI,0.043676,1,Mg,g/s 50 | PM25,PSO4,0.09631,1,Mg,g/s 51 | PM25,PTI,1.97E-03,1,Mg,g/s 52 | SO2,SO2,1,64,Mg,mol/s 53 | SO2,SULF,0,98,Mg,mol/s 54 | NH3,NH3,1,17,Mg,mol/s 55 | CO,CO,1,28,Mg,mol/s 56 | PMC,PMC,1,1,Mg,g/s 57 | NOx,NO,0.9,30,Mg,mol/s 58 | NOx,NO2,0.092,46,Mg,mol/s 59 | NOx,HONO,0.008,47,Mg,mol/s 60 | OC,POC,1,1,Mg,g/s 61 | -------------------------------------------------------------------------------- /species/MEIC-SAPRC07_SAPRC07_speciate_residential.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | SAPRC07_AACD,AACD,1,60.05,Mmol,mol/s 3 | SAPRC07_ACET,ACET,1,58.08,Mmol,mol/s 4 | SAPRC07_ACYE,ACYE,1,26.04,Mmol,mol/s 5 | SAPRC07_ALK1,ALK1,1,30.07,Mmol,mol/s 6 | SAPRC07_ALK2,ALK2,1,36.73,Mmol,mol/s 7 | SAPRC07_ALK3,ALK3,1,58.61,Mmol,mol/s 8 | SAPRC07_ALK4,ALK4,1,77.6,Mmol,mol/s 9 | SAPRC07_ALK5,ALK5,1,118.89,Mmol,mol/s 10 | SAPRC07_ARO1,ARO1,1,95.16,Mmol,mol/s 11 | SAPRC07_ARO2,ARO2MN,1,118.72,Mmol,mol/s 12 | SAPRC07_BACL,BACL,1,86.09,Mmol,mol/s 13 | SAPRC07_BALD,BALD,1,106.13,Mmol,mol/s 14 | SAPRC07_BENZ,BENZ,1,78.11,Mmol,mol/s 15 | SAPRC07_CH4,CH4,1,16.04,Mmol,mol/s 16 | SAPRC07_CCHO,CCHO,1,44.05,Mmol,mol/s 17 | SAPRC07_CRES,CRES,1,108.14,Mmol,mol/s 18 | SAPRC07_ETHE,ETHE,1,28.05,Mmol,mol/s 19 | SAPRC07_FACD,FACD,1,46.03,Mmol,mol/s 20 | SAPRC07_GLY,GLY,1,58.04,Mmol,mol/s 21 | SAPRC07_HCHO,HCHO,1,30.03,Mmol,mol/s 22 | SAPRC07_IPRD,IPRD,1,100.12,Mmol,mol/s 23 | SAPRC07_ISOP,ISOP,1,68.12,Mmol,mol/s 24 | SAPRC07_MACR,MACR,1,70.09,Mmol,mol/s 25 | SAPRC07_MEK,MEK,1,72.11,Mmol,mol/s 26 | SAPRC07_MEOH,MEOH,1,32.04,Mmol,mol/s 27 | SAPRC07_MGLY,MGLY,1,72.04,Mmol,mol/s 28 | SAPRC07_MVK,MVK,1,70.09,Mmol,mol/s 29 | SAPRC07_OLE1,OLE1,1,72.34,Mmol,mol/s 30 | SAPRC07_OLE2,OLE2,1,75.78,Mmol,mol/s 31 | SAPRC07_PACD,PACD,1,74.08,Mmol,mol/s 32 | SAPRC07_PRD2,PRD2,1,116.16,Mmol,mol/s 33 | SAPRC07_RCHO,RCHO,1,58.08,Mmol,mol/s 34 | SAPRC07_RNO3,RNO3,1,147.18,Mmol,mol/s 35 | SAPRC07_TERP,TERP,1,136.24,Mmol,mol/s 36 | PM25,PAL,5.60E-03,1,Mg,g/s 37 | PM25,PCA,0.033303,1,Mg,g/s 38 | PM25,PCL,0.009179,1,Mg,g/s 39 | PM25,PFE,6.22E-02,1,Mg,g/s 40 | PM25,PH2O,3.71E-03,1,Mg,g/s 41 | PM25,PK,0.002276,1,Mg,g/s 42 | PM25,PMG,4.07E-02,1,Mg,g/s 43 | PM25,PMN,5.00E-04,1,Mg,g/s 44 | PM25,PMOTHR,0.264368,1,Mg,g/s 45 | PM25,PNA,2.99E-03,1,Mg,g/s 46 | PM25,PNCOM,0.375107,1,Mg,g/s 47 | PM25,PNH4,0.041369,1,Mg,g/s 48 | PM25,PNO3,0.016703,1,Mg,g/s 49 | PM25,PSI,0.043676,1,Mg,g/s 50 | PM25,PSO4,0.09631,1,Mg,g/s 51 | PM25,PTI,1.97E-03,1,Mg,g/s 52 | SO2,SO2,1,64,Mg,mol/s 53 | SO2,SULF,0,98,Mg,mol/s 54 | NH3,NH3,1,17,Mg,mol/s 55 | CO,CO,1,28,Mg,mol/s 56 | PMC,PMC,1,1,Mg,g/s 57 | NOx,NO,0.9,30,Mg,mol/s 58 | NOx,NO2,0.092,46,Mg,mol/s 59 | NOx,HONO,0.008,47,Mg,mol/s 60 | OC,POC,1,1,Mg,g/s 61 | -------------------------------------------------------------------------------- /species/MEIC-SAPRC07_SAPRC07_speciate_transportation.csv: -------------------------------------------------------------------------------- 1 | pollutant,emission_species,split_factor,divisor,inv_unit,emi_unit 2 | SAPRC07_AACD,AACD,1,60.05,Mmol,mol/s 3 | SAPRC07_ACET,ACET,1,58.08,Mmol,mol/s 4 | SAPRC07_ACYE,ACYE,1,26.04,Mmol,mol/s 5 | SAPRC07_ALK1,ALK1,1,30.07,Mmol,mol/s 6 | SAPRC07_ALK2,ALK2,1,36.73,Mmol,mol/s 7 | SAPRC07_ALK3,ALK3,1,58.61,Mmol,mol/s 8 | SAPRC07_ALK4,ALK4,1,77.6,Mmol,mol/s 9 | SAPRC07_ALK5,ALK5,1,118.89,Mmol,mol/s 10 | SAPRC07_ARO1,ARO1,1,95.16,Mmol,mol/s 11 | SAPRC07_ARO2,ARO2MN,1,118.72,Mmol,mol/s 12 | SAPRC07_BACL,BACL,1,86.09,Mmol,mol/s 13 | SAPRC07_BALD,BALD,1,106.13,Mmol,mol/s 14 | SAPRC07_BENZ,BENZ,1,78.11,Mmol,mol/s 15 | SAPRC07_CH4,CH4,1,16.04,Mmol,mol/s 16 | SAPRC07_CCHO,CCHO,1,44.05,Mmol,mol/s 17 | SAPRC07_CRES,CRES,1,108.14,Mmol,mol/s 18 | SAPRC07_ETHE,ETHE,1,28.05,Mmol,mol/s 19 | SAPRC07_FACD,FACD,1,46.03,Mmol,mol/s 20 | SAPRC07_GLY,GLY,1,58.04,Mmol,mol/s 21 | SAPRC07_HCHO,HCHO,1,30.03,Mmol,mol/s 22 | SAPRC07_IPRD,IPRD,1,100.12,Mmol,mol/s 23 | SAPRC07_ISOP,ISOP,1,68.12,Mmol,mol/s 24 | SAPRC07_MACR,MACR,1,70.09,Mmol,mol/s 25 | SAPRC07_MEK,MEK,1,72.11,Mmol,mol/s 26 | SAPRC07_MEOH,MEOH,1,32.04,Mmol,mol/s 27 | SAPRC07_MGLY,MGLY,1,72.04,Mmol,mol/s 28 | SAPRC07_MVK,MVK,1,70.09,Mmol,mol/s 29 | SAPRC07_OLE1,OLE1,1,72.34,Mmol,mol/s 30 | SAPRC07_OLE2,OLE2,1,75.78,Mmol,mol/s 31 | SAPRC07_PACD,PACD,1,74.08,Mmol,mol/s 32 | SAPRC07_PRD2,PRD2,1,116.16,Mmol,mol/s 33 | SAPRC07_RCHO,RCHO,1,58.08,Mmol,mol/s 34 | SAPRC07_RNO3,RNO3,1,147.18,Mmol,mol/s 35 | SAPRC07_TERP,TERP,1,136.24,Mmol,mol/s 36 | PM25,PAL,5.60E-03,1,Mg,g/s 37 | PM25,PCA,0.033303,1,Mg,g/s 38 | PM25,PCL,0.009179,1,Mg,g/s 39 | PM25,PFE,6.22E-02,1,Mg,g/s 40 | PM25,PH2O,3.71E-03,1,Mg,g/s 41 | PM25,PK,0.002276,1,Mg,g/s 42 | PM25,PMG,4.07E-02,1,Mg,g/s 43 | PM25,PMN,5.00E-04,1,Mg,g/s 44 | PM25,PMOTHR,0.264368,1,Mg,g/s 45 | PM25,PNA,2.99E-03,1,Mg,g/s 46 | PM25,PNCOM,0.375107,1,Mg,g/s 47 | PM25,PNH4,0.041369,1,Mg,g/s 48 | PM25,PNO3,0.016703,1,Mg,g/s 49 | PM25,PSI,0.043676,1,Mg,g/s 50 | PM25,PSO4,0.09631,1,Mg,g/s 51 | PM25,PTI,1.97E-03,1,Mg,g/s 52 | SO2,SO2,1,64,Mg,mol/s 53 | SO2,SULF,0,98,Mg,mol/s 54 | NH3,NH3,1,17,Mg,mol/s 55 | CO,CO,1,28,Mg,mol/s 56 | PMC,PMC,1,1,Mg,g/s 57 | NOx,NO,0.9,30,Mg,mol/s 58 | NOx,NO2,0.092,46,Mg,mol/s 59 | NOx,HONO,0.008,47,Mg,mol/s 60 | OC,POC,1,1,Mg,g/s 61 | -------------------------------------------------------------------------------- /year2month.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/3/22 17:02 4 | # @Author :Haofan Wang 5 | # @Email :wanghf58@mail2.sysu.edu.cn 6 | import glob 7 | import os.path 8 | 9 | import pandas as pd 10 | from src import encode_title_tiff 11 | import rasterio 12 | import numpy as np 13 | 14 | if __name__ == "__main__": 15 | """ 16 | At first, your title of these files are required this format as following: 17 | "UKE_1970_00__AGS__nh3.tiff" 18 | LABEL_YEAR_00__SECTOR__POLLUTANT.tiff 19 | """ 20 | print("This script is written by Haofan Wang.") 21 | # Input directory including emission raster. 22 | input_dir = r"D:\Emission-Inventory\EDGAR\GTiff4IAT_year_reclassification\1970" 23 | output_dir = r"D:\Emission-Inventory\EDGAR\GTiff4IAT_year_reclassification\monthly-1970" 24 | os.system(f"mkdir {output_dir}") 25 | 26 | # Search the file in the input directory. 27 | files = glob.glob(f"{input_dir}/*.tiff") 28 | factor = pd.read_csv("temporal/monthly.csv") 29 | for file in files: 30 | print(file) 31 | # Open and read the raster as array. 32 | dataset = rasterio.open(file) 33 | data = dataset.read(1) 34 | file_info = encode_title_tiff(os.path.basename(file)) 35 | label = file_info["label"] 36 | year = file_info["year"] 37 | month = file_info["month"] 38 | sector = file_info["sector"] 39 | species = file_info["pollutant"] 40 | for month_i in range(1, 13): 41 | temp_factor = factor.iloc[np.where(factor.monthly.values == month_i)][sector].values[0] 42 | new_data = data * temp_factor 43 | mm = "%.2d" % month_i 44 | output_name = f"{output_dir}/{label}_{year}_{mm}__{sector}__{species}.tiff" 45 | with rasterio.open( 46 | output_name, 47 | 'w', 48 | driver='GTiff', 49 | height=new_data.shape[0], 50 | width=new_data.shape[1], 51 | count=1, 52 | dtype=new_data.dtype, 53 | crs='+proj=latlong', 54 | transform=dataset.transform, 55 | ) as dst: 56 | dst.write(new_data, 1) 57 | 58 | 59 | 60 | -------------------------------------------------------------------------------- /calculate-pmc.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import rasterio as rio 3 | import rasterio.errors 4 | 5 | if __name__ == "__main__": 6 | """ 7 | Sometimes there are only inventory of PM2.5 and PM10. 8 | But in the CMAQ model, we need the PMC to input rather PM10, 9 | thus, we should calculate the PMC inventory via PM2.5 and PM10. 10 | """ 11 | # Set the input directory. 12 | input_dir = r"D:\GitHub\MEIAT-CMAQ-data\MIX-2010" 13 | 14 | # Set the inventory prefix. 15 | prefix = "MIX" 16 | 17 | # Set the months. 18 | months = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"] 19 | # months = ["00"] 20 | 21 | # Set the sectors. 22 | 23 | sectors = ['residential', 'transportation', 'power', 'industry', 'agriculture'] 24 | 25 | # Set the years. 26 | years = [2010] 27 | 28 | for month in months: 29 | for sector in sectors: 30 | for year in years: 31 | pm25_file = f"{input_dir}/{prefix}_{year}_{month}__{sector}__PM25.tiff" 32 | pm10_file = f"{input_dir}/{prefix}_{year}_{month}__{sector}__PM10.tiff" 33 | try: 34 | pm25_dataset = rio.open(pm25_file) 35 | pm10_dataset = rio.open(pm10_file) 36 | except rasterio.errors.RasterioIOError: 37 | print(f"skip: {pm25_file} & {pm10_file}") 38 | continue 39 | 40 | height = pm25_dataset.height 41 | width = pm25_dataset.width 42 | transform = pm25_dataset.transform 43 | 44 | pm25_data = pm25_dataset.read(1) 45 | pm10_data = pm10_dataset.read(1) 46 | pmc_data = pm10_data - pm25_data 47 | 48 | # Check the PMC data. 49 | pmc_data = np.where(pmc_data < 0, 0, pmc_data) 50 | 51 | tiffile = f"{input_dir}/{prefix}_{year}_{month}__{sector}__PMC.tiff" 52 | 53 | with rio.open(tiffile, 54 | 'w', 55 | driver='GTiff', 56 | height=height, 57 | width=width, 58 | count=1, 59 | dtype=pmc_data.dtype, 60 | crs='+proj=latlong', 61 | transform=transform, ) as dst: 62 | dst.write(pmc_data, 1) -------------------------------------------------------------------------------- /PREP/meic_2_GeoTiff.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/4/29 10:48 4 | # @Author :Jiaxin Qiu 5 | 6 | import glob 7 | import os.path 8 | 9 | import tqdm 10 | import numpy as np 11 | import rasterio 12 | from rasterio.transform import from_bounds 13 | from rasterio.crs import CRS 14 | import re 15 | 16 | if __name__ == "__main__": 17 | print("This script is written by Jiaxin Qiu.") 18 | # ------------------------------------------ 19 | input_dir = r"D:\GitHub\MEIAT-CMAQ-data\MEIC_SPARC07_2013" 20 | output_dir = r"D:\GitHub\MEIAT-CMAQ-data\G-MEIC_SPARC07_2013" 21 | # ------------------------------------------ 22 | 23 | if os.path.exists(output_dir) is False: 24 | os.mkdir(output_dir) 25 | 26 | files = glob.glob(f"{input_dir}/*.asc") 27 | 28 | for file in tqdm.tqdm(files): 29 | sub_name = os.path.basename(file) 30 | condition = f"(.*?)_(.*?)_(.*?)_(.*?).asc" 31 | # condition = f"(.*?)_(.*?)__(.*?)__(.*?).asc" # For 2019 and 2020. 32 | encode_name = re.findall(condition, sub_name)[0] 33 | year = r"%.4d" % int(encode_name[0]) 34 | mm = r"%.2d" % int(encode_name[1]) 35 | sector = encode_name[2] 36 | pollutant = encode_name[3].replace(".", "") 37 | output_name = f"{output_dir}/MEIC_{year}_{mm}__{sector}__{pollutant}.tiff" 38 | 39 | # 以只读模式打开文件 40 | with open(file, 'r', encoding='utf-8') as file: 41 | # 逐行读取文件内容，并以空格为分隔符分割每行，最后将其添加到数组（列表）中 42 | lines = [line.strip().split() for index, line in enumerate(file) if index >= 6] 43 | 44 | # 打印读取到的数组（列表） 45 | _ = np.array(lines, dtype="float") 46 | z = np.where(_ == -9999.0, 0.0, _) 47 | 48 | # 最大最小经纬度 49 | min_long, min_lat, max_long, max_lat = 70.0, 10.0, 150.0, 60.0 50 | 51 | # 分辨率 52 | x_resolution = 0.25 53 | y_resolution = 0.25 54 | 55 | # 计算栅格的行和列 56 | width = int((max_long - min_long) / x_resolution) 57 | height = int((max_lat - min_lat) / y_resolution) 58 | 59 | # 创建GeoTIFF文件的变换矩阵 60 | transform = from_bounds(min_long, min_lat, max_long, max_lat, width, height) 61 | 62 | # 定义GeoTIFF文件的元数据 63 | metadata = { 64 | "driver": "GTiff", 65 | "height": height, 66 | "width": width, 67 | "count": 1, 68 | "dtype": rasterio.float32, 69 | "crs": CRS.from_epsg(4326), 70 | "transform": transform, 71 | } 72 | 73 | # 创建GeoTIFF文件 74 | with rasterio.open(output_name, "w", **metadata) as dst: 75 | dst.write(z, 1) # 将数据写入波段1 76 | 77 | 78 | 79 | 80 | -------------------------------------------------------------------------------- /PREP/mix_2_GeoTiff.py: -------------------------------------------------------------------------------- 1 | # !/usr/bin/env python 2 | # -*-coding:utf-8 -*- 3 | # @Time : 2023/01/14 16:01 4 | # @Author : Haofan Wang 5 | # @Version : python3.9 6 | # @Email : wanghf58@mail2.sysu.edu.cn 7 | 8 | import glob 9 | import os 10 | import re 11 | 12 | import xarray as xr 13 | import tqdm 14 | 15 | import rasterio as rio 16 | from rasterio.transform import Affine 17 | 18 | 19 | if __name__ == '__main__': 20 | # The path of MIX emissions inventory. 21 | fdir = r"E:\MEIC\MIX\MIX_2010_Origin" 22 | output_dir = r"E:\MEIC\MIX\MIX_2010_Origin\MIX-2010" 23 | 24 | # Set the year of emission inventory. 25 | year = 2010 26 | 27 | if os.path.exists(output_dir) is False: 28 | os.mkdir(output_dir) 29 | 30 | # Search the files. 31 | files = glob.glob(f"{fdir}/*.nc") 32 | 33 | # Get the name of pollutants. 34 | for file in tqdm.tqdm(files): 35 | ds = xr.open_dataset(file) 36 | lats = ds.coords["lat"].__array__() 37 | lons = ds.coords["lon"].__array__() 38 | lonmin, latmax, lonmax, latmin = lons.min(), lats.max(), lons.max(), lats.min() 39 | num_lon = lons.shape[0] 40 | num_lat = lats.shape[0] 41 | res = 0.25 42 | transform = Affine.translation(lonmin - res / 2, latmin - res / 2) * Affine.scale(res, res) 43 | 44 | # Set sectors. 45 | sectors = ["POWER", "INDUSTRY", "RESIDENTIAL", "TRANSPORT", "AGRICULTURE"] 46 | file_name = os.path.basename(file) 47 | condition = fr"MICS_Asia_(.*?)_{year}_0.25x0.25.nc" 48 | pollutant = re.findall(condition, file_name)[0] 49 | 50 | for var_name in list(ds.keys()): 51 | var = ds[var_name] 52 | months = var.__getattr__("time").values 53 | for i in range(12): 54 | month = "%.2d" % months[i] 55 | temp_var = var[i, ...].values 56 | sector = var_name.split("_")[-1] 57 | 58 | if sector == "TRANSPORT": 59 | sector_label = "transportation" 60 | else: 61 | sector_label = sector.lower() 62 | 63 | tiffile = f"MIX_{year}_{month}__{sector_label}__{pollutant}.tiff" 64 | with rio.open(f"{output_dir}/{tiffile}", 65 | 'w', 66 | driver='GTiff', 67 | height=num_lat, 68 | width=num_lon, 69 | count=1, 70 | dtype=temp_var.dtype, 71 | crs='+proj=latlong', 72 | transform=transform, ) as dst: 73 | dst.write(temp_var, 1) 74 | # print(f"Finish and output {output_dir}/{tiffile}.") 75 | -------------------------------------------------------------------------------- /UTIL/combine/combine.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/5/18 14:52 4 | # @Author :Haofan Wang 5 | # @Email :wanghf58@mail2.sysu.edu.cn 6 | 7 | from src import * 8 | 9 | if __name__ == "__main__": 10 | upper_raster_dir = r"D:\GitHub\MEIAT-CMAQ-data\MEIC-2017" 11 | bottom_raster_dir = r"D:\GitHub\MEIAT-CMAQ-data\MIX-2010" 12 | output_dir = r"D:\GitHub\MEIAT-CMAQ-data\MIX&MEIC" 13 | 14 | upper_raster_pollutants = ["NH3", "NOx", "PMcoarse", "PM25", "SO2", "CO", 15 | "CB05_ALD2", "CB05_ALDX", "CB05_CH4", "CB05_ETH", "CB05_ETHA", "CB05_ETOH", "CB05_FORM", 16 | "CB05_IOLE", "CB05_ISOP", "CB05_MEOH", "CB05_NVOL", "CB05_OLE", "CB05_PAR", "CB05_TERP", 17 | "CB05_TOL", "CB05_UNR", "CB05_XYL"] 18 | bottom_raster_pollutants = ["NH3", "NOx", "PMC", "PM25", "SO2", "CO", 19 | "CB05_ALD2", "CB05_ALDX", "CB05_CH4", "CB05_ETH", "CB05_ETHA", "CB05_ETOH", "CB05_FORM", 20 | "CB05_IOLE", "CB05_ISOP", "CB05_MEOH", "CB05_NVOL", "CB05_OLE", "CB05_PAR", "CB05_TERP", 21 | "CB05_TOL", "CB05_UNR", "CB05_XYL"] 22 | output_pollutants = ["NH3", "NOx", "PMC", "PM25", "SO2", "CO", 23 | "CB05_ALD2", "CB05_ALDX", "CB05_CH4", "CB05_ETH", "CB05_ETHA", "CB05_ETOH", "CB05_FORM", 24 | "CB05_IOLE", "CB05_ISOP", "CB05_MEOH", "CB05_NVOL", "CB05_OLE", "CB05_PAR", "CB05_TERP", 25 | "CB05_TOL", "CB05_UNR", "CB05_XYL"] 26 | 27 | upper_label = "MEIC" 28 | bottom_label = "MIX" 29 | output_label = "MEIC&MIX" 30 | 31 | upper_raster_template = fr"{upper_raster_dir}\MEIC_2017_01__agriculture__NH3.tiff" 32 | bottom_raster_template = fr"{bottom_raster_dir}\MIX_2010_01__industry__CB05_TOL.tiff" 33 | 34 | upper_resolution = 0.25 35 | bottom_resolution = 0.25 36 | 37 | sectors = ["power", "transportation", "residential", "industry", "agriculture"] 38 | 39 | upper_year = 2017 40 | bottom_year = 2010 41 | output_year = 2017 42 | # ------------------------------------------------------------------------------------------------------------------ 43 | 44 | start_time = time.time() 45 | main_mosaic( 46 | upper_raster_dir, 47 | bottom_raster_dir, 48 | output_dir, 49 | upper_raster_pollutants, 50 | bottom_raster_pollutants, 51 | output_pollutants, 52 | upper_label, 53 | bottom_label, 54 | output_label, 55 | upper_raster_template, 56 | bottom_raster_template, 57 | upper_resolution, 58 | bottom_resolution, 59 | sectors, 60 | upper_year, 61 | bottom_year, 62 | output_year, 63 | ) 64 | end_time = time.time() 65 | elapsed_time = end_time - start_time 66 | print(f"### Time consuming: {elapsed_time} s ###") 67 | -------------------------------------------------------------------------------- /PREP/CEDSv2_2_GeoTiff.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | # @Time :2023/4/15 15:48 4 | # @Author :Jiaxin Qiu 5 | 6 | import glob 7 | import os.path 8 | 9 | import rasterio 10 | import xarray as xr 11 | import pyproj 12 | import numpy as np 13 | 14 | """ 15 | Data access: https://edgar.jrc.ec.europa.eu/dataset_ap61 16 | """ 17 | 18 | if __name__ == '__main__': 19 | print("This script is written by Jiaxin Qiu.") 20 | 21 | input_dir = r"I:\CEDSv2\2013" 22 | output_dir = r"I:\CEDSv2\2013\GTiff" 23 | 24 | if os.path.exists(output_dir) is False: 25 | os.mkdir(output_dir) 26 | 27 | files = glob.glob(f"{input_dir}/*.nc") 28 | for file in files: 29 | # 定义WGS84等经纬度投影 30 | crs_proj = pyproj.CRS.from_string('EPSG:4326') 31 | 32 | # 读取NetCDF数据集 33 | ds = xr.open_dataset(file) 34 | 35 | sub_name = os.path.basename(file) 36 | yyyy = sub_name.split("_")[-1].split(".")[0] 37 | 38 | # 获取所有数据变量列表 39 | variables = list(ds.data_vars.keys()) 40 | # print(variables) 41 | 42 | for variable in variables: 43 | data_array = ds[variable] 44 | sector = variable.split("_")[1] 45 | pollutant = variable.split("_")[0] 46 | 47 | for month in range(1, data_array.shape[0]+1): 48 | month = r"%.2d" % month 49 | temp_data_array = data_array[int(month)-1, ...] 50 | # print(temp_data_array) 51 | # 获取空间信息 52 | height, width = temp_data_array.shape[0], temp_data_array.shape[1] 53 | lats, lons = temp_data_array.coords["lat"], temp_data_array.coords["lon"] 54 | south, north = lats.values.min(), lats.values.max() 55 | # south, north = lats.values.max(), lats.values.min() 56 | west, east = lons.values.min(), lons.values.max() 57 | # print(height, width, south, north, west, east) 58 | 59 | temp_data_array = np.flipud(np.where(temp_data_array < 0, 0, temp_data_array)) 60 | 61 | # MEIAT-CMAQ: LABEL_YYYY_MM__SECTOR__POLUTANT.tiff 62 | _sub_name = f"CEDS_{yyyy}_{month}__{sector}__{pollutant}.tiff" 63 | # print(temp_data_array) 64 | # # 创建GTiff文件 65 | with rasterio.open(f'{output_dir}/{_sub_name}.tiff', 'w', driver='GTiff', 66 | width=width, height=height, count=1, 67 | dtype=temp_data_array.dtype.name, nodata=0, 68 | transform=rasterio.transform.from_bounds(west, south, east, north, width, height), 69 | crs=crs_proj) as dst: 70 | # 将NetCDF数据写入GTiff 71 | dst.write(temp_data_array * 10E-3 * 50 * 50 * 1000000 * 2592000, 1) 72 | print(f'{output_dir}/{_sub_name}') 73 | # exit() 74 | 75 | 76 | -------------------------------------------------------------------------------- /Doc/adopt_local_emission_to_meic_cn.md: -------------------------------------------------------------------------------- 1 | # 在MEIC清单中使用本地排放源 2 | 3 | -------------------- 4 | 5 | **作者：王浩帆** 6 | 7 | -------------------- 8 | 9 | 10 | 在开始此教程以前，我们已经有了一份广州市的本地工业排放清单（表）。 11 | 12 | | Pollutant Name | Total Amount (Mg) | 13 | | -------------- | ----------------- | 14 | | NOx | 1000 | 15 | | SO2 | 2000 | 16 | 17 | 18 | ## 步骤1：配置namelist.input 19 | 20 | 主要注意以下两点： 21 | 22 | * 修改`big_grid_file`为：`big_grid_file = "shapefile/CN-City.shp"` 23 | * 修改`create_source`为：`create_source = 0,` 24 | 25 | ``` 26 | &global 27 | griddesc_file = "input/GRIDDESC.PRD274x181" 28 | griddesc_name = "PRD274x181" 29 | big_grid_file = "shapefile/CN-City.shp" 30 | geotiff_dir = "H:/MEIC/GeoTiff-2017" 31 | inventory_label = "MEIC" 32 | inventory_year = "2017" 33 | sectors = 'transportation', 'residential', 'power', 'agriculture', 'industry' 34 | allocator = 'line', 'landscan-global-2017_nodata.tif', 'power.tif', 'agriculture.tif', 'industry.tif', 35 | allocator_type = "line", "raster", "raster", "raster", "raster" 36 | inventory_mechanism = "MEIC-CB05" 37 | target_mechanism = "CB06" 38 | start_date = "2020-07-01" 39 | end_date = "2020-07-02" 40 | cores = 4 41 | / 42 | 43 | &line 44 | line_files = "motorway.shp", "primary.shp", "residential.shp", "secondary.shp" 45 | line_factors = 0.435798, 0.326848, 0.081712, 0.155642, 46 | / 47 | 48 | &control 49 | create_grid = 1, 50 | grid_info = 1, 51 | create_factor = 1, 52 | coarse_emission = 1, 53 | create_source = 0, 54 | / 55 | ``` 56 | 57 | ## 步骤2：运行`coarse_emission_2_fine_emission.py` 58 | 59 | 在终端中输入命令： 60 | 61 | ```shell 62 | python coarse_emission_2_fine_emission.py 63 | ``` 64 | 65 | ## 步骤3：打开`output\zoning_statistics\MEIC_2017_07__industry__*` 66 | 67 | 1. 打开`output\zoning_statistics\MEIC_2017_07__industry__NOx.csv`，将箭头所值位置改为本地清单中的NOx排放量（1000）: 68 | 69 | ![修改区域统计结果-NOx](zonalst_nox.png) 70 | 71 | 72 | 同理打开`output\zoning_statistics\MEIC_2017_07__industry__SO2.csv`，修改广州市的SO2排放量。 73 | 74 | ## 步骤4：重新配置namelist.input 75 | 76 | * 只需要修改`&control`部分即可。 77 | 78 | ``` 79 | &global 80 | griddesc_file = "input/GRIDDESC.PRD274x181" 81 | griddesc_name = "PRD274x181" 82 | big_grid_file = "shapefile/CN-City.shp" 83 | geotiff_dir = "H:/MEIC/GeoTiff-2017" 84 | inventory_label = "MEIC" 85 | inventory_year = "2017" 86 | sectors = 'transportation', 'residential', 'power', 'agriculture', 'industry' 87 | allocator = 'line', 'landscan-global-2017_nodata.tif', 'power.tif', 'agriculture.tif', 'industry.tif', 88 | allocator_type = "line", "raster", "raster", "raster", "raster" 89 | inventory_mechanism = "MEIC-CB05" 90 | target_mechanism = "CB06" 91 | start_date = "2020-07-01" 92 | end_date = "2020-07-02" 93 | cores = 4 94 | / 95 | 96 | &line 97 | line_files = "motorway.shp", "primary.shp", "residential.shp", "secondary.shp" 98 | line_factors = 0.435798, 0.326848, 0.081712, 0.155642, 99 | / 100 | 101 | &control 102 | create_grid = 0, 103 | grid_info = 0, 104 | create_factor = 0, 105 | coarse_emission = 0, 106 | create_source = 1, 107 | / 108 | ``` 109 | 110 | ## 步骤5：运行`coarse_emission_2_fine_emission.py` 111 | 112 | 在终端中输入命令： 113 | 114 | ```shell 115 | python coarse_emission_2_fine_emission.py 116 | ``` 117 | 118 | ## 步骤6：运行`Create-CMAQ-Emission-File.py` 119 | 120 | 在终端中输入命令： 121 | 122 | ```shell 123 | python Create-CMAQ-Emission-File.py 124 | ``` -------------------------------------------------------------------------------- /Doc/adopt_meic_for_prd_emission_file_cn.md: -------------------------------------------------------------------------------- 1 | # 使用2017年的MEIC清单制作珠三角的排放文件 2 | 3 | -------------------- 4 | 5 | **作者：王浩帆** 6 | 7 | -------------------- 8 | 9 | 本教程通过一个制作珠江三角洲模拟域（空间分辨率：3km）的排放清单来讲解以下两个程序的运行流程。 10 | 1. [coarse_emission_2_fine_emission.py](../coarse_emission_2_fine_emission.py) 11 | 2. [Create-CMAQ-Emission-File.py](../Create-CMAQ-Emission-File.py) 12 | 13 | 本教程使用的原始排放清单为2017年MEIC清单（逐月、分部门、CB05机制），输出的排放文件为2017年7月1日至2017年7月2日的分部门排放文件（CB06，AERO7机制）。 14 | 15 | ## 第一步：准备`GRIDDESC`文件 16 | 17 | `GRIDDESC`由CMAQ的前处理系统MCIP输出，主要需要注意以下两点： 18 | * 保证每一个`GRIDDESC`中有且仅有一个网格信息。 19 | * 保证GRIDDESC中的网格投影为Lambert投影（只要你的WRF设置为Lambert并正常运行MCIP，基本都不会有错）。 20 | * 如本教程所示的`GRIDDESC`文件中，`PRD274x181`是网格名称（`griddesc_name`: 此参数将会被`namelist.input`用到）。 21 | 22 | ```shell 23 | ' ' 24 | 'LamCon_40N_97W' 25 | 2 25.000 40.000 110.000 110.000 28.500 26 | ' ' 27 | 'PRD274x181' 28 | 'LamCon_40N_97W' 48000.000 -902500.000 3000.000 3000.000 271 178 1 29 | ' ' 30 | ``` 31 | 32 | ## 第二步：准备MEIC排放清单 33 | 34 | 下载地址：http://meicmodel.org/ 35 | 36 | 由于MEIC存在版权保护，因此本程序中不提供数据，请用户自行下载。 37 | 38 | 下载到的MEIC通常会提供包括`*.asc`在内的一种或几种格式（如图），但本程序所有的网格化文件，都要求使用WGS1984投影的GeoTIFF文件。 39 | 40 | MEIC下载获取到的文件目录 41 | 42 | 因此需要通过[MEIC转GEOTIFF工具](../PREP/meic_2_GeoTiff.py)来对MEIC进行转换。在此程序中，输入的文件仅为`*.asc`格式。 43 | 44 | 通过以下方法配置好代码以后，在终端中运行`python ./PREP/meic_2_GeoTiff.py`即可。 45 | ```python 46 | # ------------------------------------------ 47 | input_dir = "MEIC的asc格式文件所在目录路径" 48 | output_dir = "输出文件所在目录路径" 49 | # ------------------------------------------ 50 | ``` 51 | 52 | 运行成功以后，将会在输出路径下看到系列GeoTIFF格式文件。 53 | 54 | ![转换后的文件](converted_meic_files.png) 55 | 56 | ## 第三步：配置`namelist.input`文件。 57 | 58 | 准备好`GRIDDESC`文件和MEIC的GeoTIFF文件以后就可以开始配置`namelist.input`来准备运行程序了。 59 | 60 | 在配置`namelist.input`文件之前，需要强调一点：**整个程序对文件命名的要求都十分严格，请按要求给定GeoTIFF文件的命名**。 61 | 62 | 命名规则为： `{LABEL}_{YYYY}_{MM}__{SECTOR}__{POLLUTANT}.tiff` 63 | * LABEL：可以自行指定，但不能包含`_`。 64 | * YYYY：清单年份，4位字符串。 65 | * MM：清单月份，2位字符串。 66 | * SECTOR：部门名称，**注意：SECTOR的前后是两个`_`,而不是一个。** 67 | * POLLUTANT：物种名称。 68 | 69 | **注意：TIFF中的单位必须是`Mg/month`或者`Mmol/month`。** 70 | 71 | 以下是一个`namelist.input`的示例： 72 | 73 | 1. 网格和文件名信息。 74 | * griddesc_file：`GRIDDESC`文件所在路径。 75 | * griddesc_name：`GRIDDESC`文件中的网格名称。 76 | * big_grid_file：粗网格的shapefile文件，该文件中必须含有`NAME`字段，类型为字符串类型，WGS1984投影。 77 | * geotiff_dir：GeoTIFF文件所在目录路径。 78 | * inventory_label：原始清单文件名中的`${LABEL}`。 79 | * inventory_year：原始清单文件名中的`${YYYY}`。 80 | 81 | 2. 空间分配信息（以下参数保证一一对应）。 82 | * sectors： 分配部门，部门为GeoTIFF文件名中所提到的`{SECTOR}`。 83 | * allocator：分配因子，分配因子所在路径必须放在[allocator](../allocator)目录中，当对应的分配类型为`line`时，读取`&line`部分所设置的参数，此处分配因子任意填写。 84 | * allocator_type：分配类型，支持`raster`分配和`line`分配。 85 | 86 | *&line*部分 87 | * line_files：存放于[allocator](../allocator)目录中线数据名称。 88 | * line_factors：`line_files`中的线数据将按照此比例进行组合分配。 89 | 90 | 3. 物种分配谱信息。 91 | * inventory_mechanism：字符串，配合`target_mechanism`使用，用于寻找对应的物种分配文件，不能含有`_`。 92 | * target_mechanism：字符串，配合`inventory_mechanism`使用，用于寻找对应的物种分配文件，不能含有`_`。 93 | **注意：物种分配谱文件存放于[species](../species)中，且物种分配文件的命名规则为：{inventory_mechanism}_{target_mechanism}_speciate_{SECTOR}** 94 | 95 | 4. 排放文件日期 96 | * start_date：排放文件的开始日期，格式：YYYY-MM-DD 97 | * end_date：排放文件的结束日期，格式：YYYY-MM-DD 98 | 99 | 5. 并行计算核心数 100 | * cores：根据自己的电脑配置设置。 101 | 102 | 6. `&control`开关控制部分 103 | * create_grid：控制生成模拟域网格shapefile。 104 | * grid_info：控制生成网格信息表格。 105 | * create_factor：控制生成排放因子文件。 106 | * coarse_emission：控制粗网格的区域统计。 107 | * create_source：控制生成source文件。 108 | 109 | ```Fortran 110 | &global 111 | griddesc_file = "input/GRIDDESC.PRD274x181" 112 | griddesc_name = "PRD274x181" 113 | big_grid_file = "shapefile/MEIC-0P25.shp" 114 | geotiff_dir = "H:\MEIC\GeoTiff-2017" 115 | inventory_label = "MEIC" 116 | inventory_year = "2017" 117 | sectors = 'transportation', 'residential', 'power', 'agriculture', 'industry' 118 | allocator = 'line', 'population-nodata-0.tif', 'ave-nodata-0.tif', 'ave-nodata-0.tif', 'ave-nodata-0.tif', 119 | allocator_type = "line", "raster", "raster", "raster", "raster" 120 | inventory_mechanism = "MEIC-CB05" 121 | target_mechanism = "CB06" 122 | start_date = "2020-07-01" 123 | end_date = "2020-07-02" 124 | cores = 4 125 | / 126 | 127 | &line 128 | line_files = "motorway.shp", "primary.shp", "residential.shp", "secondary.shp" 129 | line_factors = 0.435798, 0.326848, 0.081712, 0.155642, 130 | / 131 | 132 | &control 133 | create_grid = 1, 134 | grid_info = 1, 135 | create_factor = 1, 136 | coarse_emission = 1, 137 | create_source = 1, 138 | / 139 | ``` 140 | 配置好`namelist.input`以后进入第四步。 141 | 142 | ## 第四步：完成空间分配。 143 | 144 | 将[allocator](../allocator)目录下的压缩文件解压。 145 | 146 | 该过程通过[coarse_emission_2_fine_emission.py](../coarse_emission_2_fine_emission.py)实现，输入以下命令执行脚本。 147 | 148 | ```shell 149 | python .\coarse_emission_2_fine_emission.py 150 | ``` 151 | 152 | ## 第五步：完成时间分配和物种分配，并生成直接输入CMAQ的排放文件。 153 | 154 | 该过程通过[Create-CMAQ-Emission-File.py](../Create-CMAQ-Emission-File.py)实现，输入以下命令执行脚本。 155 | 156 | ```shell 157 | python .\Create-CMAQ-Emission-File.py 158 | ``` 159 | 160 | 虽然在[output](../output)中成功生成了排放文件，但是在日志中发现了如下警告信息。 161 | 162 | ```shell 163 | Warning: Do not have the pollutant named PMC. 164 | ``` 165 | 166 | 这是因为在对应部门的物种文件中第一列的`PMC`无法找到（如图） 167 | 168 | ![物种文件截图](speices_warning.png) 169 | 170 | 根本原因是因为，GeoTIFF所在目录下的`PMC`物种的命名为`PMcoarse`. 171 | ![img.png](meic_2017_PMcoarse.png) 172 | 173 | **解决方案** 174 | 1. 将对应部门物种文件中第一列的`PMC`改为`PMcoarse`，重新运行第五步。 175 | 2. 使用[rename_original_inventory_(pollutant).py](../UTIL/rename_original_inventory/rename_original_inventory_(pollutant).py)将`PMcoarse`改为`PMC`即可。 176 | 修改`namelist.input`中的`geotiff_dir`为新的GeoTIFF目录，重新运行第四步和第五步即可。 177 | 178 | [rename_original_inventory_(pollutant).py](../UTIL/rename_original_inventory/rename_original_inventory_(pollutant).py)使用方法见[原始排放清单重命名使用手册](../UTIL/rename_original_inventory/README.md)。 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | -------------------------------------------------------------------------------- /src.py: -------------------------------------------------------------------------------- 1 | # import datetime 2 | # import glob 3 | # import multiprocessing 4 | import os 5 | # import os.path 6 | # import re 7 | import time 8 | # import shutil 9 | # 10 | # import PseudoNetCDF as pnc 11 | # import arcgisscripting 12 | # import arcpy 13 | # import f90nml 14 | # import geopandas as gpd 15 | # import numpy as np 16 | # import pandas as pd 17 | # import pyioapi 18 | # import pyproj 19 | # import rioxarray as rxr 20 | # import tqdm 21 | # from arcpy.sa import * 22 | # from shapely.geometry import Polygon, Point 23 | # from shapely.ops import cascaded_union 24 | # from shapely.prepared import prep 25 | # 26 | # import shapefile as shp 27 | 28 | # import arcgisscripting 29 | # import arcpy 30 | # from arcpy.sa import * 31 | # # Use half of the cores on the machine 32 | # arcpy.env.parallelProcessingFactor = "50%" 33 | # arcpy.env.overwriteOutput = True 34 | import pandas as pd 35 | 36 | 37 | def user_control(): 38 | import datetime 39 | specified_time = datetime.datetime(2500, 6, 30, 23, 59) 40 | # 获取当前时间 41 | current_time = datetime.datetime.now() 42 | # 检查当前时间是否已经超过指定时间 43 | if current_time > specified_time: 44 | return False 45 | else: 46 | return True 47 | 48 | 49 | def time_format(): 50 | import datetime 51 | return f'{datetime.datetime.now()}|> ' 52 | 53 | 54 | def main_mosaic( 55 | upper_raster_dir, 56 | bottom_raster_dir, 57 | output_dir, 58 | upper_raster_pollutants, 59 | bottom_raster_pollutants, 60 | output_pollutants, 61 | upper_label, 62 | bottom_label, 63 | output_label, 64 | upper_raster_template, 65 | bottom_raster_template, 66 | upper_resolution, 67 | bottom_resolution, 68 | sectors, 69 | upper_year, 70 | bottom_year, 71 | output_year, 72 | ): 73 | # -------------------------------------------------------------------------------------------------------- 74 | if user_control() is True: 75 | print("### This system is developed by Haofan Wang. ###") 76 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 77 | else: 78 | print("### This system is developed by Haofan Wang. ###") 79 | print("### You can contact me for any suggestions. ###") 80 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 81 | print("### *************************************************** ###") 82 | print("### The current version has expired. ###") 83 | print("### Please contact me to request the latest version. ###") 84 | print("### *************************************************** ###") 85 | return 86 | # -------------------------------------------------------------------------------------------------------- 87 | 88 | import rasterio 89 | import arcpy 90 | import shutil 91 | 92 | arcpy.env.parallelProcessingFactor = "50%" 93 | arcpy.env.overwriteOutput = True 94 | 95 | factor = bottom_resolution ** 2 / upper_resolution ** 2 96 | 97 | if os.path.exists(output_dir) is False: 98 | os.mkdir(output_dir) 99 | 100 | for month in range(1, 13): 101 | month = r"%.2d" % month 102 | for sector in sectors: 103 | for upper_raster_pollutant, bottom_raster_pollutant, output_pollutant in zip(upper_raster_pollutants, 104 | bottom_raster_pollutants, 105 | output_pollutants): 106 | upper_raster_file = f"{upper_raster_dir}/{upper_label}_{upper_year}_{month}__{sector}__{upper_raster_pollutant}.tiff" 107 | bottom_raster_file = f"{bottom_raster_dir}/{bottom_label}_{bottom_year}_{month}__{sector}__{bottom_raster_pollutant}.tiff" 108 | 109 | upper_raster_file_test = os.path.exists(upper_raster_file) 110 | bottom_raster_file_test = os.path.exists(bottom_raster_file) 111 | 112 | if upper_raster_file_test is False: 113 | with rasterio.open(upper_raster_template) as src: 114 | raster_data = src.read(1) 115 | output_meta = src.meta.copy() 116 | values = raster_data * 0 117 | with rasterio.open(upper_raster_file, "w", **output_meta) as dest: 118 | # 将相加后的数据写入新的栅格文件 119 | dest.write(values, 1) 120 | if bottom_raster_file_test is False: 121 | with rasterio.open(bottom_raster_template) as src: 122 | raster_data = src.read(1) 123 | output_meta = src.meta.copy() 124 | values = raster_data * 0 125 | with rasterio.open(bottom_raster_file, "w", **output_meta) as dest: 126 | # 将相加后的数据写入新的栅格文件 127 | dest.write(values, 1) 128 | 129 | with rasterio.open(bottom_raster_template) as src: 130 | raster_data = src.read(1) 131 | output_meta = src.meta.copy() 132 | with rasterio.open(bottom_raster_file, "w", **output_meta) as dest: 133 | # 将相加后的数据写入新的栅格文件 134 | dest.write(raster_data / factor, 1) 135 | 136 | output_name = f"{output_dir}/{output_label}_{output_year}_{month}__{sector}__{output_pollutant}.tif" 137 | if os.path.exists(output_name): 138 | os.remove(output_name) 139 | shutil.copy(bottom_raster_file, output_name) 140 | 141 | # 使用arcpy.sa.MosaicToNewRaster()函数将两个输入栅格镶嵌到一个新的栅格文件中 142 | arcpy.Mosaic_management([output_name, upper_raster_file], 143 | output_name, 144 | mosaic_type="LAST", 145 | nodata_value=0, ) 146 | # 删除多余文件 147 | if os.path.exists(output_name[0:-4] + ".tfw"): 148 | os.remove(output_name[0:-4] + ".tfw") 149 | if os.path.exists(output_name + ".aux.xml"): 150 | os.remove(output_name + ".aux.xml") 151 | if os.path.exists(output_name + ".xml"): 152 | os.remove(output_name + ".xml") 153 | if os.path.exists(output_name + ".ovr"): 154 | os.remove(output_name + ".ovr") 155 | shutil.move(output_name, output_name + "f") 156 | print(f"Finish and output {output_name}f.") 157 | 158 | 159 | def main_vertical_allocation(files, sectors): 160 | # -------------------------------------------------------------------------------------------------------- 161 | if user_control() is True: 162 | print("### This system is developed by Haofan Wang. ###") 163 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 164 | else: 165 | print("### This system is developed by Haofan Wang. ###") 166 | print("### You can contact me for any suggestions. ###") 167 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 168 | print("### *************************************************** ###") 169 | print("### The current version has expired. ###") 170 | print("### Please contact me to request the latest version. ###") 171 | print("### *************************************************** ###") 172 | return 173 | # -------------------------------------------------------------------------------------------------------- 174 | import f90nml 175 | import multiprocessing 176 | # -------------------------------------------------------------------------------------------------------- 177 | path = os.getcwd() 178 | output_dir = fr"{path}/output/vertical" 179 | if os.path.exists(output_dir) is False: 180 | os.mkdir(output_dir) 181 | 182 | # Set the parallel cores. 183 | example = f90nml.read("namelist.input") 184 | cores = example["global"]["cores"] 185 | 186 | # Obtain the available core number. 187 | num_cores = multiprocessing.cpu_count() 188 | print("The total core: ", num_cores) 189 | print("Your set is: ", cores) 190 | if cores > num_cores: 191 | print("Please ensure that the number of cores used " 192 | "does not exceed the maximum number of cores on your computer.") 193 | exit() 194 | 195 | # Create a threading pool that use the all cores. 196 | pool = multiprocessing.Pool(cores) 197 | 198 | # Build a argument pool. 199 | arg_pool = [] 200 | for file in files: 201 | for sector in sectors: 202 | arg = (file, output_dir, sector) 203 | arg_pool.append(arg) 204 | 205 | # Start cores. 206 | results = pool.starmap(vertical_allocation, arg_pool) 207 | 208 | # Close the thread pool. 209 | pool.close() 210 | pool.join() 211 | 212 | print("Done") 213 | 214 | # def vertical_allocation(eipath, output_dir): 215 | def vertical_allocation(eipath, output_dir, sector): # Modify by Haofan Wang. 216 | import PseudoNetCDF as pnc 217 | # import pandas as pd 218 | # import numpy as np 219 | # -------------------------------------------------------------------------------------------------------- 220 | if os.path.exists(output_dir) is False: 221 | os.mkdir(output_dir) 222 | e2df = pnc.pncopen(eipath, format='ioapi') 223 | varliststr = getattr(e2df, 'VAR-LIST') 224 | varlist = [varliststr[i:i + 16].strip() for i in range(0, len(varliststr), 16)] 225 | profpath = 'profile.csv' 226 | profile = pd.read_csv(profpath) 227 | nz = profile.shape[0] 228 | # outpath = 'example_3d_emissions.nc' 229 | outpath = f"{output_dir}/{os.path.basename(eipath)}" 230 | verbose = 0 231 | e3df = e2df.slice(LAY=[0] * nz) 232 | e3df.VGLVLS = profile.vglvltop.values.astype('f') 233 | e3df.NLAYS = nz 234 | e3df.VGTOP = e2df.VGTOP 235 | for key, var3d in e3df.variables.items(): 236 | if key not in varlist: 237 | continue 238 | var2d = e2df.variables[key] 239 | vals2d = var2d[:] 240 | # vals3d = vals2d * profile['fraction'].values[None, :, None, None] 241 | vals3d = vals2d * profile[sector].values[None, :, None, None] # Modify by Haofan Wang. 242 | var3d[:] = vals3d 243 | 244 | if os.path.exists(outpath): 245 | os.remove(outpath) 246 | outf = e3df.save(outpath, verbose=verbose, complevel=1, format="NETCDF3_CLASSIC") 247 | outf.close() 248 | 249 | 250 | def main_coarse2fine(): 251 | # -------------------------------------------------------------------------------------------------------- 252 | if user_control() is True: 253 | print("### This system is developed by Haofan Wang. ###") 254 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 255 | else: 256 | print("### This system is developed by Haofan Wang. ###") 257 | print("### You can contact me for any suggestions. ###") 258 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 259 | print("### *************************************************** ###") 260 | print("### The current version has expired. ###") 261 | print("### Please contact me to request the latest version. ###") 262 | print("### *************************************************** ###") 263 | return 264 | # -------------------------------------------------------------------------------------------------------- 265 | import f90nml 266 | import PseudoNetCDF as pnc 267 | import geopandas as gpd 268 | # import pandas as pd 269 | import numpy as np 270 | # -------------------------------------------------------------------------------------------------------- 271 | path = os.getcwd() 272 | # -------------------------------------------------------------------------------------------------------- 273 | output_dir = f"{path}/output" 274 | if os.path.exists(output_dir) is False: 275 | os.mkdir(output_dir) 276 | 277 | # -------------------------------------------------------------------------------------------------------- 278 | example = f90nml.read("namelist.input") 279 | griddesc_file = example["global"]["griddesc_file"] 280 | griddesc_name = example["global"]["griddesc_name"] 281 | 282 | # -------------------------------------------------------------------------------------------------------- 283 | control_create_grid = example["control"]["create_grid"] 284 | print(f"--------------Fine grid shapefile|> {output_dir}/shapefile-grid.shp--------------") 285 | if int(control_create_grid) == 1: 286 | print( 287 | f'{time_format()}The control of create grid is {control_create_grid} and processor start creating fine grid.') 288 | draw_modle_grid(griddesc_file) 289 | gf = pnc.pncopen(griddesc_file, GDNAM=griddesc_name, format="griddesc", SDATE=2023001, TSTEP=10000, 290 | withcf=False) 291 | gpdf = gpd.read_file(f"{output_dir}/shapefile-grid.shp") 292 | lon, lat = gf.ij2ll(gpdf["colnum"].values, gpdf["rownum"].values) 293 | gpdf["LON"] = lon 294 | gpdf["LAT"] = lat 295 | gpdf.to_file(f'{output_dir}/shapefile-grid.shp', driver='ESRI Shapefile', encoding='utf-8') 296 | gpdf[["ID", "colnum", "rownum", "LON", "LAT"]].to_csv(f"{output_dir}/shapefile-grid.csv") 297 | print(f'{time_format()}Finish creating fine grid shapefile.') 298 | else: 299 | print(f"{time_format()}The control of create grid is {control_create_grid}.") 300 | if os.path.exists(f"{output_dir}/shapefile-grid.shp"): 301 | print(f"{time_format()}There is fine grid shapefile and processor will continue.") 302 | else: 303 | print(f"{time_format()}There is not fine grid shapefile and processor will exit.") 304 | exit() 305 | 306 | # -------------------------------------------------------------------------------------------------------- 307 | small_grid = f"{output_dir}/shapefile-grid.shp" 308 | big_grid = example["global"]["big_grid_file"] 309 | control_grid_info = example["control"]["grid_info"] 310 | grid_info_out_path = output_dir 311 | print(f"-----------------Grid information|> {grid_info_out_path}/grid_info.csv--------------") 312 | if int(control_grid_info) == 1: 313 | print( 314 | f'{time_format()}The control of grid information is {control_grid_info} and processor start for grid information.') 315 | if os.path.exists(grid_info_out_path) is False: 316 | os.makedirs(grid_info_out_path) 317 | max_id = pd.read_csv(f"{output_dir}/shapefile-grid.csv")["ID"].values.max() 318 | create_gridinfo(small_grid, big_grid, grid_info_out_path, max_id) 319 | grid_info_path = f'{grid_info_out_path}/grid_info.csv' 320 | print(f'{time_format()}Finish creating grid information.') 321 | else: 322 | print(f"{time_format()}The control of grid information is {control_grid_info}.") 323 | if os.path.exists(f'{grid_info_out_path}/grid_info.csv'): 324 | print(f"{time_format()}There is grid information and processor will continue.") 325 | else: 326 | print(f"{time_format()}There is not grid information and processor will exit.") 327 | exit() 328 | 329 | # -------------------------------------------------------------------------------------------------------- 330 | ef_out_path = f'{output_dir}/factor' 331 | grid_info_path = f'{grid_info_out_path}/grid_info.csv' 332 | if os.path.exists(ef_out_path) is False: 333 | os.mkdir(ef_out_path) 334 | control_create_factor = example["control"]["create_factor"] 335 | print(f"----------------Allocation factor|> {ef_out_path}--------------") 336 | # 读取分配因子类型 分配因子路径 分配部门 337 | allocator_types = example["global"]["allocator_type"] 338 | if type(allocator_types) != type(["list"]): 339 | allocator_types = [allocator_types] 340 | allocators = example["global"]["allocator"] 341 | if type(allocators) != type(["list"]): 342 | allocators = [allocators] 343 | sectors = example["global"]["sectors"] 344 | if type(sectors) != type(["list"]): 345 | sectors = [sectors] 346 | 347 | if int(control_create_factor) == 1: 348 | print( 349 | f'{time_format()}The control of allocation factor is {control_create_factor} and processor start for allocation factor.') 350 | for allocator_type, allocator, sector in zip(allocator_types, allocators, sectors): 351 | # 判断数据是否已经存在，如果存在就直接跳过。 352 | if os.path.exists(f'{ef_out_path}/EF_{sector}.csv'): 353 | print(f"{time_format()}There is {ef_out_path}/EF_{sector}.csv and processor will skip this loop.") 354 | continue 355 | print(f"{time_format()}There is the process for {sector} and the allocator type is {allocator_type}.") 356 | if allocator_type == "line": 357 | road_dir = f"{path}/allocator" 358 | line_files = example["line"]["line_files"] 359 | line_factors = example["line"]["line_factors"] 360 | print(f"{time_format()}Allocator | {line_files}.") 361 | print(f"{time_format()}Allocator factor | {line_factors}.") 362 | road_allocation(line_files, road_dir, small_grid, big_grid, grid_info_path, ef_out_path) 363 | output_name = f'{ef_out_path}/EF_{sector}.csv' 364 | ef_files = [f"{output_dir}/factor/EF_{line_file}.csv" for line_file in line_files] 365 | road_deal(ef_files, line_factors, output_name) 366 | elif allocator_type == "raster": 367 | result_csv_path_name = f'EF_{sector}' 368 | raster_file = f"{path}/allocator/{allocator}" 369 | print(f"{time_format()}Allocator | {raster_file}.") 370 | raster_allocation(raster_file, small_grid, big_grid, grid_info_path, ef_out_path, result_csv_path_name) 371 | else: 372 | print(f"{time_format()}Allocator type is not support.| {allocator_type}.") 373 | else: 374 | print(f"{time_format()}The control of allocation is {control_create_factor}.") 375 | for sector in sectors: 376 | if os.path.exists(f'{ef_out_path}/EF_{sector}.csv'): 377 | print(f"{time_format()}There is allocation factor and processor will continue.") 378 | else: 379 | print(f"{time_format()}There is not allocation factor and processor will exit.") 380 | exit() 381 | # -------------------------------------------------------------------------------------------------------- 382 | control_coarse_emis = example["control"]["coarse_emission"] 383 | print(f"----------------Coarse Emission|> f'{output_dir}/zoning_statistics'--------------") 384 | if int(control_coarse_emis) == 1: 385 | print( 386 | f'{time_format()}The control of coarse emission is {control_coarse_emis} and processor start for coarse emission.') 387 | meic_zoning_out = f'{output_dir}/zoning_statistics' 388 | if os.path.exists(meic_zoning_out) is False: 389 | os.mkdir(meic_zoning_out) 390 | geotiff_dir = example["global"]["geotiff_dir"] 391 | start_date = pd.to_datetime(example["global"]["start_date"]) 392 | end_date = pd.to_datetime(example["global"]["end_date"]) 393 | date_list = pd.date_range(start_date, end_date) 394 | mms = np.unique(np.array([temp_date.strftime("%m") for temp_date in date_list])) 395 | for mm in mms: 396 | zoning_statistics(small_grid, big_grid, geotiff_dir, meic_zoning_out, mm) 397 | else: 398 | print(f"{time_format()}The control of coarse emission is {control_coarse_emis}.") 399 | 400 | # -------------------------------------------------------------------------------------------------------- 401 | meic_zoning_out = f'{output_dir}/zoning_statistics' 402 | control_create_source = example["control"]["create_source"] 403 | source_out_dir = f'{output_dir}/source' 404 | if os.path.exists(source_out_dir) is False: 405 | os.mkdir(source_out_dir) 406 | print(f"----------------Coarse Emission|> f'{output_dir}/source'--------------") 407 | if int(control_create_source) == 1: 408 | start_date = pd.to_datetime(example["global"]["start_date"]) 409 | end_date = pd.to_datetime(example["global"]["end_date"]) 410 | date_list = pd.date_range(start_date, end_date) 411 | mms = np.unique(np.array([temp_date.strftime("%m") for temp_date in date_list])) 412 | for mm in mms: 413 | print(f"Processing for month {mm}.") 414 | inventory_label = example["global"]["inventory_label"] 415 | inventory_year = example["global"]["inventory_year"] 416 | create_source(inventory_label, inventory_year, mm, sectors, ef_out_path, meic_zoning_out, source_out_dir) 417 | print( 418 | f'{time_format()}The control of create source is {control_create_source} and processor start for coarse emission.') 419 | else: 420 | print( 421 | f'{time_format()}The control of create source is {control_create_source}.') 422 | 423 | # -------------------------------------------------------------------------------------------------------- 424 | print('# ------------------------------------' + 'End' + '------------------------------------ #') 425 | current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 426 | print(f"The program end time ：{current_time}") 427 | print('# ------------------------------------' + '---' + '------------------------------------ #') 428 | 429 | 430 | def main_create_CMAQ_mask(gadmpath, field, output_name): 431 | # -------------------------------------------------------------------------------------------------------- 432 | if user_control() is True: 433 | print("### This system is developed by Haofan Wang. ###") 434 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 435 | else: 436 | print("### This system is developed by Haofan Wang. ###") 437 | print("### You can contact me for any suggestions. ###") 438 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 439 | print("### *************************************************** ###") 440 | print("### The current version has expired. ###") 441 | print("### Please contact me to request the latest version. ###") 442 | print("### *************************************************** ###") 443 | return 444 | # -------------------------------------------------------------------------------------------------------- 445 | import f90nml 446 | import shapefile as shp 447 | import numpy as np 448 | import PseudoNetCDF as pnc 449 | from shapely.geometry import Polygon, Point 450 | from shapely.ops import cascaded_union 451 | from shapely.prepared import prep 452 | # -------------------------------------------------------------------------------------------------------- 453 | 454 | # Set the parallel cores. 455 | example = f90nml.read("namelist.input") 456 | grid_file = example["global"]["griddesc_file"] 457 | grid_name = example["global"]["griddesc_name"] 458 | 459 | # Open the shapefile. 460 | shpf = shp.Reader(gadmpath) 461 | 462 | # Find where is the field. 463 | fldnames = [n for n, t, l, d in shpf.fields][1:] 464 | try: 465 | pos = np.where(np.array(fldnames) == field)[0][0] 466 | except IndexError: 467 | exit("ERROR: No such field in the shapefile.") 468 | 469 | # All field attributes. 470 | _ = [rec[pos] for ri, rec in enumerate(shpf.iterRecords())] 471 | _ = np.unique(np.array(_)) 472 | 473 | # Open the GRIDDESC. 474 | gf = pnc.pncopen(grid_file, format='griddesc', GDNAM=grid_name) 475 | outf = gf.eval("DUMMY = DUMMY[:] * 0") 476 | 477 | for city_name in _: 478 | # Integrate the all sub-shapefile. 479 | recordnums = [ri for ri, rec in enumerate(shpf.iterRecords()) if rec[pos] == city_name] 480 | shapes = [shpf.shape(rn) for rn in recordnums] 481 | polygons = [Polygon(s.points).buffer(0) for s in shapes] 482 | ppolygons = [prep(p) for p in polygons] 483 | uberpoly = cascaded_union(polygons) 484 | puberpoly = prep(uberpoly) 485 | 486 | evar = outf.createVariable(city_name, 'f', ('TSTEP', 'LAY', 'ROW', 'COL')) 487 | 488 | Ipl1, Jpl1 = np.meshgrid(np.arange(gf.NCOLS + 1), np.arange(gf.NROWS + 1)) 489 | LONp1, LATp1 = gf.ij2ll(Ipl1, Jpl1) 490 | Xp1, Yp1 = gf.ll2xy(LONp1, LATp1) 491 | LOND, LATD = gf.xy2ll(Xp1 - gf.XCELL / 2, Yp1 - gf.YCELL / 2) 492 | LON = LONp1[:-1, :-1] 493 | LAT = LATp1[:-1, :-1] 494 | 495 | gf.SDATE = 2019001 496 | MINE = outf.variables[city_name] 497 | MINE.units = 'fraction' 498 | MINE.long_name = city_name 499 | MINE.var_desc = '0 means not mine, 1 means all mine, inbetween is partial' 500 | 501 | fractional = True 502 | for j, i in np.ndindex(gf.NROWS, gf.NCOLS): 503 | if fractional: 504 | gpoly = Polygon([ 505 | [LOND[j + 0, i + 0], LATD[j + 0, i + 0]], 506 | [LOND[j + 0, i + 1], LATD[j + 0, i + 1]], 507 | [LOND[j + 1, i + 1], LATD[j + 1, i + 1]], 508 | [LOND[j + 1, i + 0], LATD[j + 1, i + 0]], 509 | [LOND[j + 0, i + 0], LATD[j + 0, i + 0]], 510 | ]) 511 | if puberpoly.intersects(gpoly): 512 | intx = gpoly.intersection(uberpoly) 513 | farea = intx.area / gpoly.area 514 | MINE[0, 0, j, i] = farea 515 | else: 516 | gp = Point(LON[j, i], LAT[j, i]) 517 | if puberpoly.contains(gp): 518 | if uberpoly.contains(gp): 519 | MINE[0, 0, j, i] = 1 520 | 521 | # # Get rid of initial DUMMY variable 522 | del outf.variables['DUMMY'] 523 | # Remove VAR-LIST so that it can be inferred 524 | delattr(outf, 'VAR-LIST') 525 | outf.updatemeta() 526 | 527 | outf.variables['TFLAG'][:] = 0 528 | outf.SDATE = -635 529 | savedf = outf.save(output_name, verbose=0, complevel=1) 530 | savedf.close() 531 | 532 | 533 | def main_rename_original_pollutant(tbl_name, input_dir, output_dir): 534 | # -------------------------------------------------------------------------------------------------------- 535 | if user_control() is True: 536 | print("### This system is developed by Haofan Wang. ###") 537 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 538 | else: 539 | print("### This system is developed by Haofan Wang. ###") 540 | print("### You can contact me for any suggestions. ###") 541 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 542 | print("### *************************************************** ###") 543 | print("### The current version has expired. ###") 544 | print("### Please contact me to request the latest version. ###") 545 | print("### *************************************************** ###") 546 | return 547 | # -------------------------------------------------------------------------------------------------------- 548 | import glob 549 | import tqdm 550 | import numpy as np 551 | import shutil 552 | # -------------------------------------------------------------------------------------------------------- 553 | 554 | if os.path.exists(output_dir) is False: 555 | os.mkdir(output_dir) 556 | 557 | files = glob.glob(f"{input_dir}/*.tiff") 558 | 559 | for file in tqdm.tqdm(files): 560 | sub_name = encode_title_tiff(os.path.basename(file)) 561 | label = sub_name["label"] 562 | year = sub_name["year"] 563 | month = sub_name["month"] 564 | sector = sub_name["sector"] 565 | pollutant = sub_name["pollutant"] 566 | 567 | try: 568 | pos = np.where(np.array(tbl_name["org"]) == pollutant)[0][0] 569 | except IndexError: 570 | shutil.copy(file, f"{output_dir}/{os.path.basename(file)}") 571 | continue 572 | 573 | new_name = tbl_name["new"][pos] 574 | 575 | new_file_name = f"{label}_{year}_{month}__{sector}__{new_name}.tiff" 576 | output_path = f"{output_dir}/{new_file_name}" 577 | shutil.copy(file, output_path) 578 | 579 | 580 | def create_emission_file( 581 | emission_date, 582 | grid_desc, 583 | grid_name, 584 | label, 585 | sector, 586 | inventory_year, 587 | inventory_mechanism, 588 | target_mechanism): 589 | # -------------------------------------------------------------------------------------------------------- 590 | # import pandas as pd 591 | import datetime 592 | import PseudoNetCDF as pnc 593 | import numpy as np 594 | # -------------------------------------------------------------------------------------------------------- 595 | # Convert the emission date to other format. 596 | datetime_emission = pd.to_datetime(emission_date) 597 | yyyymmdd = datetime.datetime.strftime(datetime_emission, "%Y%m%d") 598 | # yyyy = datetime.datetime.strftime(datetime_emission, "%Y") 599 | mm = datetime.datetime.strftime(datetime_emission, "%m") 600 | # dd = datetime.datetime.strftime(datetime_emission, "%d") 601 | yyyyjjj = datetime.datetime.strftime(datetime_emission, "%Y%j") 602 | w = datetime.datetime.strftime(datetime_emission, "%w") 603 | 604 | # Create template file. 605 | gf = pnc.pncopen(grid_desc, GDNAM=grid_name, format="griddesc", SDATE=int(yyyyjjj), TSTEP=10000, withcf=False) 606 | gf.updatetflag(overwrite=True) 607 | tmpf = gf.sliceDimensions(TSTEP=[0] * 25) 608 | max_col_index = getattr(tmpf, "NCOLS") - 1 609 | max_row_index = getattr(tmpf, "NROWS") - 1 610 | 611 | # Create the source file and read it. 612 | source_file = f"output/source/source-{label}-{sector}-{inventory_year}-{mm}.csv" 613 | data = pd.read_csv(source_file) 614 | 615 | # Add I and J coordinate and calculate the total emission. 616 | I, J = gf.ll2ij(data["LON"].values, data["LAT"].values) 617 | # data["I"], data["J"] = data["rownum"], data["colnum"] 618 | data["I"], data["J"] = I, J 619 | celltotal = data.groupby(["I", "J"], as_index=False).sum() 620 | celltotal = celltotal[ 621 | (celltotal["I"] >= 0) 622 | & (celltotal["J"] >= 0) 623 | & (celltotal["I"] <= max_col_index) 624 | & (celltotal["J"] <= max_row_index) 625 | ] 626 | # Read species file. 627 | species_file = ( 628 | f"species/{inventory_mechanism}_{target_mechanism}_speciate_{sector}.csv" 629 | ) 630 | species_info = pd.read_csv(species_file) 631 | fname_list = species_info.pollutant.values 632 | var_list = species_info.emission_species.values 633 | factor_list = species_info.split_factor.values 634 | divisor_list = species_info.divisor.values 635 | origin_units = species_info.inv_unit.values 636 | target_units = species_info.emi_unit.values 637 | 638 | # Read the temporal file. 639 | # _monthly_factor = pd.read_csv("temporal/monthly.csv") 640 | _weekly_factor = pd.read_csv("temporal/weekly.csv") 641 | _hourly_factor = pd.read_csv("temporal/hourly.csv") 642 | # monthly_factor = _monthly_factor[sector].values 643 | weekly_factor = _weekly_factor[sector].values 644 | hourly_factor = _hourly_factor[sector].values 645 | 646 | # Loop the species and create the variable to IOAPI file. 647 | items = zip( 648 | fname_list, var_list, factor_list, divisor_list, origin_units, target_units 649 | ) 650 | for fname, var, split_factor, divisor, origin_unit, target_unit in items: 651 | try: 652 | # Extract the current pollutant. 653 | df = celltotal[["I", "J", fname]] 654 | 655 | # Convert monthly emission to weekly emission. 656 | weekly_values = df[fname].values * 0.25 # Version 1.0 and Version 1.1 657 | # weekly_values = converted_values * 0.25 658 | 659 | # Convert weekly emission to daily emission. 660 | daily_values = weekly_values * weekly_factor[int(w)] 661 | 662 | # Convert daily emission to hourly emission. 663 | df_list = [] 664 | for hour_i in range(24): 665 | _df = pd.DataFrame(columns=["J", "I", "hour", "values"]) 666 | _df["J"] = df.J.values 667 | _df["I"] = df.I.values 668 | _df["hour"] = np.zeros(df.shape[0]) + hour_i 669 | _df["values"] = daily_values * hourly_factor[hour_i] 670 | df_list.append(_df) 671 | result = pd.concat(df_list) 672 | # print(result) 673 | 674 | # Convert original units to target units and input the split_factor. 675 | if origin_unit == "Mmol" and target_unit == "mol/s": 676 | result["values"] = result["values"] * 1000000.0 / 3600.0 * split_factor 677 | elif origin_unit == "Mg" and target_unit == "g/s": 678 | result["values"] = result["values"] * 1000000.0 / 3600.0 * split_factor 679 | elif origin_unit == "Mg" and target_unit == "mol/s": 680 | result["values"] = ( 681 | result["values"] * 1000000.0 / 3600.0 / divisor * split_factor 682 | ) 683 | 684 | # Convert the I, J, hour to int. 685 | result[["hour", "J", "I"]] = result[["hour", "J", "I"]].astype("int") 686 | h = result.hour 687 | i = result.I 688 | j = result.J 689 | 690 | # Create the variable of emission. 691 | evar = tmpf.createVariable(var, "f", ("TSTEP", "LAY", "ROW", "COL")) 692 | if target_unit == "mol/s": 693 | evar.setncatts(dict(units="moles/s", long_name=var, var_desc=var)) 694 | elif target_unit == "g/s": 695 | evar.setncatts(dict(units="g/s", long_name=var, var_desc=var)) 696 | 697 | evar[h, h * 0, j, i] = result["values"].values 698 | 699 | except KeyError: 700 | # If don not have this pollutant in GeoTIFF, skip it. 701 | print(f"Warning: Do not have the pollutant named {fname}.") 702 | continue 703 | # Get rid of initial DUMMY variable 704 | del tmpf.variables["DUMMY"] 705 | 706 | # Update TFLAG to be consistent with variables 707 | tmpf.updatetflag(tstep=10000, overwrite=True) 708 | 709 | # Remove VAR-LIST so that it can be inferred 710 | delattr(tmpf, "VAR-LIST") 711 | tmpf.updatemeta() 712 | 713 | # Save out. 714 | output_name = f"output/{target_mechanism}_{sector}_{grid_name}_{yyyymmdd}.nc" 715 | tmpf.save(output_name, format="NETCDF3_CLASSIC") 716 | 717 | 718 | def create_gridinfo(small_grid, big_grid, out_path, max_id): 719 | # -------------------------------------------------------------------------------------------------------- 720 | import geopandas as gpd 721 | import numpy as np 722 | import glob 723 | # -------------------------------------------------------------------------------------------------------- 724 | # 删除文件夹下的输出文件 725 | try: 726 | os.remove(f'{out_path}/cmaq_grid.csv') 727 | os.remove(f'{out_path}/cmaq_intersect.csv') 728 | # current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 729 | # print(f'{current_time}: 输出目录已完成清理，开始进行网格信息核算。') 730 | except: 731 | current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 732 | # print(f'{current_time} 输出目录已完成清理，开始进行网格信息核算。') 733 | 734 | out_feature_class = f'{out_path}\cmaq_intersect.shp' 735 | gdf_smallgrid = gpd.read_file(small_grid).to_crs(epsg=4326) 736 | gdf_biggrid = gpd.read_file(big_grid).to_crs(gdf_smallgrid.crs) 737 | intersect_out = gpd.overlay(gdf_smallgrid, gdf_biggrid,how='intersection') 738 | intersect_out.to_file(out_feature_class) 739 | gdf = gpd.read_file(small_grid) 740 | out_name = 'cmaq_grid.csv' 741 | gdf.to_csv(f'{out_path}/{out_name}', index=False) 742 | 743 | # 获取经纬度 744 | df = pd.read_csv(f'{out_path}/{out_name}') 745 | df_lon = df['LON'].values 746 | df_lat = df['LAT'].values 747 | 748 | # max_id = df['ID'].values.max() 749 | gdf = gpd.read_file(out_feature_class) 750 | gdf = gdf.to_crs(epsg=4547) 751 | gdf['square'] = gdf.geometry.area / 1e6 752 | gdf.to_csv(f'{out_path}/cmaq_intersect.csv', index=False) 753 | 754 | # 此代码块则为删除面积占比较小的属性条 755 | df = pd.read_csv(f'{out_path}/cmaq_intersect.csv') 756 | df_ids = df['ID'].values 757 | df_area = df['square'].values 758 | 759 | del_list = [] 760 | # Delete the min value. 761 | for temp_id in range(int(max_id)+1): 762 | if df_ids.__contains__(temp_id): 763 | pos_in_df = np.where(df_ids == temp_id)[0] 764 | temp_area = df_area[pos_in_df] 765 | pos_in_temp = np.where(temp_area != temp_area.max())[0] 766 | if len(pos_in_temp) != 0: 767 | pos = pos_in_df[pos_in_temp] 768 | for temp_pos in pos: 769 | del_list.append(temp_pos) 770 | else: 771 | unknown_gird = pd.DataFrame({'ID': temp_id, 772 | 'NAME': 'unknown', 773 | 'LON': df_lon[temp_id-1], 774 | 'LAT': df_lat[temp_id-1]}, 775 | index=[0]) 776 | df = df.append(unknown_gird, ignore_index=True) 777 | 778 | df = df.drop(del_list) 779 | df = df.drop_duplicates(['ID']) 780 | df.sort_values("ID", inplace=True) 781 | df.to_csv(f'{out_path}/grid_info.csv', index=False) 782 | 783 | # 删除临时文件 784 | 785 | file_list = glob.glob(f'{out_path}/cmaq_intersect.*') 786 | for file in file_list: 787 | os.remove(file) 788 | 789 | file_list = glob.glob(f'{out_path}/cmaq_grid.*') 790 | for file in file_list: 791 | os.remove(file) 792 | 793 | 794 | # ------------------------------------------------------------------- 795 | # 空间分配因子计算-道路分配 796 | # ------------------------------------------------------------------- 797 | def road_allocation(road_class_list, road_dir, small_grid, big_grid, grid_info, out_path): 798 | # ------------------------------------------------------------------- 799 | import numpy as np 800 | import geopandas as gpd 801 | from shapely.geometry import box 802 | import glob 803 | # ------------------------------------------------------------------- 804 | # out_path = r'E:\chengdu\分配因子' # 设置输出目录路径 805 | 806 | # ------------------------------------------------------------------- 807 | # 空间分配因子计算-道路分配 808 | # ------------------------------------------------------------------- 809 | for road_class in road_class_list: 810 | 811 | road_file = fr'{road_dir}\{road_class}' # 设置用于分配的线数据路径 812 | # print(f'-> 当前处理数据 【 {road_file} 】') 813 | out_csv_shp_name = f'EF_{road_class}' # 设置输出的文件名称 814 | 815 | try: 816 | os.remove(f'{out_path}/Big_grid_road.csv') 817 | os.remove(f'{out_path}/Small_grid_road.csv') 818 | except: 819 | current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 820 | 821 | gdf_road = gpd.read_file(road_file) 822 | 823 | # 获取smallgrid的边界box 824 | gdf_clip_grid = gpd.read_file(small_grid).to_crs(gdf_road.crs) 825 | clip_bounds = gdf_clip_grid.total_bounds # 获取grid的边界，用于获得裁剪roadshp的box 826 | clip_box = box(clip_bounds[0], clip_bounds[1], clip_bounds[2], clip_bounds[3]) 827 | box_gdf = gpd.GeoDataFrame(geometry=[clip_box], crs=gdf_clip_grid.crs) # 将box转化为gdf 828 | 829 | # 计算大网格中的道路总长 830 | # Clip 831 | clipped_big_grid = f'{out_path}/Big_grid_cliped.shp' 832 | clip_out = gpd.overlay(box_gdf, gpd.read_file(big_grid).to_crs(gdf_road.crs), how='intersection') 833 | clip_out.to_file(clipped_big_grid) 834 | 835 | out_feature_class = f'{out_path}/Big_grid_road.shp' 836 | clip_big_grid = gpd.read_file(clipped_big_grid).to_crs(gdf_road.crs) 837 | gdf_road_clip = gpd.overlay(gdf_road, box_gdf,how='intersection') # 先用gird边界的box进行裁剪，再进行裁剪，以提高运行速度，通常进行空间分配的fine domian都较小 838 | intersect_out = gpd.overlay(gdf_road_clip, clip_big_grid, how='intersection') 839 | intersect_out.to_file(out_feature_class) 840 | 841 | gdf = gpd.read_file(out_feature_class) 842 | gdf = gdf.to_crs(epsg=4547) #4547 843 | gdf = gdf.explode() 844 | gdf['Length_m'] = gdf.geometry.length 845 | result = gdf.groupby('NAME', as_index=False)['Length_m'].sum() 846 | MULTILINESTRING_combine = pd.merge(result, gdf[['NAME']].drop_duplicates(), on='NAME') 847 | MULTILINESTRING_combine.to_csv(f'{out_path}/Big_grid_road.csv', index=False) 848 | 849 | temp_big_grid_sst = pd.read_csv(f'{out_path}/Big_grid_road.csv') 850 | name_list = np.unique(temp_big_grid_sst['NAME']) 851 | length_list = [] 852 | for temp_area in name_list: 853 | temp_length = temp_big_grid_sst[temp_big_grid_sst['NAME'].isin([temp_area])]['Length_m'].values.sum() 854 | length_list.append(temp_length) 855 | big_grid_sst = pd.DataFrame(columns=['NAME', 'LENGTH']) 856 | big_grid_sst['NAME'] = name_list 857 | big_grid_sst['LENGTH'] = length_list 858 | 859 | # 计算小网格中的道路总长 860 | out_feature_class = f'{out_path}/Small_grid_road.shp' 861 | clip_small_out = gpd.overlay(gdf_road_clip, gpd.read_file(small_grid).to_crs(gdf_road.crs), 862 | how='intersection') 863 | clip_small_out.to_file(out_feature_class) 864 | 865 | gdf = gpd.read_file(out_feature_class) 866 | gdf = gdf.to_crs(epsg=4547) # 867 | gdf['Length_m'] = gdf.geometry.length 868 | gdf.to_csv(f'{out_path}/Small_grid_road.csv', index=False) 869 | 870 | temp_small_grid_sst = pd.read_csv(f'{out_path}/Small_grid_road.csv') 871 | id_list = np.unique(temp_small_grid_sst['ID']) 872 | length_list = [] 873 | for temp_id in id_list: 874 | temp_length = temp_small_grid_sst[temp_small_grid_sst['ID'].isin([temp_id])]['Length_m'].values.sum() 875 | length_list.append(temp_length) 876 | small_grid_sst = pd.DataFrame(columns=['ID', 'LENGTH']) 877 | small_grid_sst['ID'] = id_list 878 | small_grid_sst['LENGTH'] = length_list 879 | 880 | df = pd.read_csv(grid_info) 881 | result = pd.DataFrame(columns=['ID', 'LON', 'LAT', 'AREA', 'EF']) 882 | ef_list = [] 883 | id_list = [] 884 | area_list = [] 885 | lon_list = [] 886 | lat_list = [] 887 | for temp_id, temp_area, temp_lon, temp_lat in zip(df['ID'].values, df['NAME'].values, df['LON'].values, 888 | df['LAT'].values): 889 | 890 | if temp_area == 'unknown': 891 | temp_ef = 0.0 892 | 893 | ef_list.append(temp_ef) 894 | id_list.append(temp_id) 895 | area_list.append(temp_area) 896 | lon_list.append(temp_lon) 897 | lat_list.append(temp_lat) 898 | continue 899 | 900 | # print(temp_area) 901 | if small_grid_sst['ID'].values.__contains__(temp_id): 902 | temp_length = small_grid_sst[small_grid_sst['ID'].isin([temp_id])]['LENGTH'].values[0] 903 | try: 904 | temp_total_length = big_grid_sst[big_grid_sst['NAME'].isin([temp_area])]['LENGTH'].values[0] 905 | temp_ef = temp_length / temp_total_length 906 | except: 907 | temp_ef = 0.0 908 | else: 909 | temp_ef = 0.0 910 | 911 | ef_list.append(temp_ef) 912 | id_list.append(temp_id) 913 | area_list.append(temp_area) 914 | lon_list.append(temp_lon) 915 | lat_list.append(temp_lat) 916 | 917 | result['ID'] = id_list 918 | result['LON'] = lon_list 919 | result['LAT'] = lat_list 920 | result['AREA'] = area_list 921 | result['EF'] = ef_list 922 | 923 | out_name = f'{out_csv_shp_name}.csv' 924 | result_csv_path = f'{out_path}/{out_name}' 925 | result.to_csv(result_csv_path, index=False) 926 | 927 | # 删除临时文件 928 | file_list = glob.glob(f'{out_path}/Big_grid_cliped.*') 929 | for file in file_list: 930 | os.remove(file) 931 | 932 | file_list = glob.glob(f'{out_path}/Big_grid_road.*') 933 | for file in file_list: 934 | os.remove(file) 935 | 936 | file_list = glob.glob(f'{out_path}/Small_grid_road.*') 937 | for file in file_list: 938 | os.remove(file) 939 | 940 | 941 | def road_deal(ef_files, ef_factors, output_name): 942 | # ------------------------------------------------------------------- 943 | # import pandas as pd 944 | # ------------------------------------------------------------------- 945 | result_ef = pd.read_csv(ef_files[0])['EF'] * 0 946 | for ef_file, ef_factor in zip(ef_files, ef_factors): 947 | file_0 = pd.read_csv(ef_file)['EF'] 948 | result_ef += file_0 * ef_factor 949 | df = pd.read_csv(ef_files[0]) 950 | df['EF'] = result_ef 951 | df.to_csv(output_name, index=False) 952 | 953 | def raster_allocation(raster_file, small_grid, big_grid, grid_info, out_path, result_csv_path_name): 954 | # ------------------------------------------------------------------- 955 | # import pandas as pd 956 | import glob 957 | import geopandas as gpd 958 | import rasterstats 959 | from shapely.geometry import box 960 | # ------------------------------------------------------------------- 961 | try: 962 | os.remove(f'{out_path}/Big_grid_zonalstattblout.csv') 963 | os.remove(f'{out_path}/Small_grid_zonalstattblout.csv') 964 | except: 965 | current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 966 | 967 | # 获取smallgrid的box边界用于clip 968 | gdf_clip_grid = gpd.read_file(small_grid).to_crs(epsg=4326) 969 | clip_bounds = gdf_clip_grid.total_bounds # 获取grid的边界，用于获得裁剪roadshp的box 970 | clip_box = box(clip_bounds[0], clip_bounds[1], clip_bounds[2], clip_bounds[3]) 971 | box_gdf = gpd.GeoDataFrame(geometry=[clip_box], crs=gdf_clip_grid.crs) # 将box转化为gdf 972 | 973 | clipped_big_grid = f'{out_path}/Big_grid_cliped.shp' 974 | clip_out = gpd.overlay(box_gdf, gpd.read_file(big_grid).to_crs(epsg=4326), how='intersection') 975 | clip_out.to_file(clipped_big_grid) 976 | 977 | outZSaT = rasterstats.zonal_stats(clipped_big_grid, raster_file, stats="sum", geojson_out=True, nodata=-999) 978 | result = gpd.GeoDataFrame.from_features(outZSaT) 979 | result = result.rename(columns={'sum': 'SUM'}) 980 | out_name = 'Big_grid_zonalstattblout.csv' 981 | result.to_csv(f'{out_path}/{out_name}', index=False) 982 | 983 | gdf_small_grid = gpd.read_file(small_grid).to_crs(epsg=4326) 984 | outZSaT = rasterstats.zonal_stats(gdf_small_grid, raster_file, stats="sum", geojson_out=True, nodata=-999) 985 | result = gpd.GeoDataFrame.from_features(outZSaT) 986 | result = result.rename(columns={'sum': 'SUM'}) 987 | out_name = 'Small_grid_zonalstattblout.csv' 988 | result.to_csv(f'{out_path}/{out_name}', index=False) 989 | 990 | df = pd.read_csv(grid_info) 991 | big_grid_zstt = pd.read_csv(f'{out_path}\Big_grid_zonalstattblout.csv') 992 | small_grid_zstt = pd.read_csv(f'{out_path}\Small_grid_zonalstattblout.csv') 993 | ef_list = [] 994 | id_list = [] 995 | area_list = [] 996 | lon_list = [] 997 | lat_list = [] 998 | for temp_id, temp_area, temp_lon, temp_lat in zip(df['ID'].values, df['NAME'].values, df['LON'].values, 999 | df['LAT'].values): 1000 | if temp_area == 'unknown': 1001 | temp_ef = 0.0 1002 | 1003 | ef_list.append(temp_ef) 1004 | id_list.append(temp_id) 1005 | area_list.append(temp_area) 1006 | lon_list.append(temp_lon) 1007 | lat_list.append(temp_lat) 1008 | continue 1009 | 1010 | try: 1011 | temp_big_grid_length = big_grid_zstt[big_grid_zstt['NAME'].isin([temp_area])]['SUM'].values[0] 1012 | except: 1013 | temp_big_grid_length = 0.0 1014 | 1015 | if temp_big_grid_length == 0.0: 1016 | temp_ef = 0.0 1017 | 1018 | ef_list.append(temp_ef) 1019 | id_list.append(temp_id) 1020 | area_list.append(temp_area) 1021 | lon_list.append(temp_lon) 1022 | lat_list.append(temp_lat) 1023 | continue 1024 | try: 1025 | temp_small_grid_length = small_grid_zstt[small_grid_zstt['ID'].isin([temp_id])]['SUM'].values[0] 1026 | # temp_big_grid_length = big_grid_zstt[big_grid_zstt['NAME'].isin([temp_area])]['SUM'].values[0] 1027 | temp_ef = temp_small_grid_length / temp_big_grid_length 1028 | except: 1029 | temp_ef = 0.0 1030 | 1031 | ef_list.append(temp_ef) 1032 | id_list.append(temp_id) 1033 | area_list.append(temp_area) 1034 | lon_list.append(temp_lon) 1035 | lat_list.append(temp_lat) 1036 | 1037 | result = pd.DataFrame(columns=['ID', 'LON', 'LAT', 'AREA', 'EF']) 1038 | result['ID'] = id_list 1039 | result['LON'] = lon_list 1040 | result['LAT'] = lat_list 1041 | result['AREA'] = area_list 1042 | result['EF'] = ef_list 1043 | 1044 | result_csv_path = f'{out_path}/{result_csv_path_name}.csv' 1045 | result.to_csv(result_csv_path, index=False) 1046 | 1047 | file_list = glob.glob(f'{out_path}/Big_grid_cliped.*') 1048 | for file in file_list: 1049 | os.remove(file) 1050 | 1051 | file_list = glob.glob(f'{out_path}/Big_grid_zonalstattblout.*') 1052 | for file in file_list: 1053 | os.remove(file) 1054 | 1055 | file_list = glob.glob(f'{out_path}/Small_grid_zonalstattblout.*') 1056 | for file in file_list: 1057 | os.remove(file) 1058 | 1059 | 1060 | def split_file_extension(filebasename): 1061 | filename, file_extension = os.path.splitext(filebasename) 1062 | return filename, file_extension 1063 | 1064 | 1065 | def zoning_statistics(small_grid, big_grid, raster_dir, out_path, mm): 1066 | 1067 | import glob 1068 | import geopandas as gpd 1069 | import rasterstats 1070 | from shapely.geometry import box 1071 | import tqdm 1072 | # Use half of the cores on the machine 1073 | 1074 | if os.path.exists(out_path) is False: 1075 | os.mkdir(out_path) 1076 | 1077 | # 获取smallgrid的边界box 1078 | gdf_clip_grid = gpd.read_file(small_grid).to_crs(epsg=4326) 1079 | clip_bounds = gdf_clip_grid.total_bounds # 获取grid的边界，用于获得裁剪roadshp的box 1080 | clip_box = box(clip_bounds[0], clip_bounds[1], clip_bounds[2], clip_bounds[3]) 1081 | box_gdf = gpd.GeoDataFrame(geometry=[clip_box], crs=gdf_clip_grid.crs) # 将box转化为gdf 1082 | 1083 | out_feature_class = f'{out_path}/Clipped.shp' 1084 | clip_out = gpd.overlay(box_gdf, gpd.read_file(big_grid).to_crs(epsg=4326), how='intersection') 1085 | clip_out.to_file(out_feature_class) 1086 | 1087 | file_list = glob.glob(f'{raster_dir}/*_*_{mm}__*__*.tiff') 1088 | if len(file_list) == 0: 1089 | exit(f"ERROR: There is no file in the directory: {raster_dir}") 1090 | 1091 | mid_out_list = [] 1092 | for file in tqdm.tqdm(file_list, desc=f"{time_format()}Processing for month {mm}"): 1093 | # out_name = os.path.basename(file).split(".")[0] 1094 | sub_name = os.path.basename(file) 1095 | out_name, file_extension = split_file_extension(sub_name) 1096 | 1097 | # 判断文件是否存在，如果存在则直接跳过。 1098 | if os.path.exists(f'{out_path}/{out_name}.csv'): 1099 | continue 1100 | try: 1101 | outZSaT = rasterstats.zonal_stats(out_feature_class, file, stats="sum", geojson_out=True, nodata=-999) 1102 | result = gpd.GeoDataFrame.from_features(outZSaT) 1103 | result = result[result['sum'].notnull()] # 删除没有算出EF的部分 1104 | result = result.rename(columns={'sum': 'SUM'}) 1105 | result.to_csv(f'{out_path}/{out_name}.csv', index=False) 1106 | mid_out_list.append(f'{out_path}/{out_name}.csv') 1107 | except : 1108 | exit(f"ERROR: Please double check the file : {file}") 1109 | 1110 | # 删除多余文件 1111 | file_list = glob.glob(f'{out_path}/*.cpg') 1112 | for file in file_list: 1113 | os.remove(file) 1114 | 1115 | file_list = glob.glob(f'{out_path}/*.xml') 1116 | for file in file_list: 1117 | os.remove(file) 1118 | 1119 | file_list = glob.glob(f'{out_path}/*.dbf') 1120 | for file in file_list: 1121 | os.remove(file) 1122 | 1123 | file_list = glob.glob(f'{out_path}/Clipped.*') 1124 | for file in file_list: 1125 | os.remove(file) 1126 | 1127 | 1128 | def draw_modle_grid(griddesc_file): 1129 | import pyioapi 1130 | import geopandas as gpd 1131 | from shapely.geometry import Polygon 1132 | import pyproj 1133 | # import pandas as pd 1134 | # 读入GRIDDESC文件 1135 | griddesc = pyioapi.GRIDDESC(griddesc_file) 1136 | # 获取网格参数 1137 | grid = griddesc.get_grid(list(griddesc.grids)[0]) 1138 | # 获取坐标系参数 1139 | coord = griddesc.get_coord(list(griddesc.coords)[0]) 1140 | # 根据网格和坐标系名设置输出名 1141 | outshp_name = list(griddesc.coords)[0] + "_" + list(griddesc.grids)[0] 1142 | 1143 | # 初始化起始格网四角范围 1144 | ringXleftOrigin = grid.XORIG 1145 | ringXrightOrigin = ringXleftOrigin + grid.XCELL 1146 | ringYbottomOrigin = grid.YORIG 1147 | ringYtopOrigin = ringYbottomOrigin + grid.YCELL 1148 | 1149 | # 遍历列，每一列写入格网 1150 | col = 1 1151 | idd_num = 1 1152 | grids_list = [] # 用于存储生成的各个网格 1153 | while col <= grid.NCOLS: 1154 | # 初始化，每一列写入完成都把上下范围初始化 1155 | ringYtop = ringYtopOrigin 1156 | ringYbottom = ringYbottomOrigin 1157 | # 遍历行，对这一列每一行格子创建和写入 1158 | row = 1 1159 | while row <= grid.NROWS: 1160 | # 创建左下角第一个格子 1161 | ring = Polygon([(ringXleftOrigin, ringYbottom), 1162 | (ringXleftOrigin, ringYtop), 1163 | (ringXrightOrigin, ringYtop), 1164 | (ringXrightOrigin, ringYbottom)]) 1165 | # 写入几何多边形 1166 | geo_df = gpd.GeoDataFrame(data=[[str(idd_num-1), row-1, col-1]], 1167 | geometry=[ring], 1168 | columns=['ID', 'rownum', 'colnum']) 1169 | grids_list.append(geo_df) 1170 | 1171 | # 下一多边形，更新上下范围 1172 | row += 1 1173 | ringYtop = ringYtop + grid.YCELL 1174 | ringYbottom = ringYbottom + grid.YCELL 1175 | 1176 | idd_num += 1 1177 | # 一列写入完成后，下一列，更新左右范围 1178 | col += 1 1179 | ringXleftOrigin = ringXleftOrigin + grid.XCELL 1180 | ringXrightOrigin = ringXrightOrigin + grid.XCELL 1181 | # 合并列表中的网格 1182 | gdf_fishgrid = pd.concat(grids_list) 1183 | 1184 | # 使用GRIDDESC的参数设置网格投影 1185 | wrf_proj = pyproj.Proj('+proj=lcc ' + '+lon_0=' + str(coord.XCENT) + ' lat_0=' + str(coord.YCENT) 1186 | + ' +lat_1=' + str(coord.P_ALP) + ' +lat_2=' + str(coord.P_BET)) 1187 | # 设置网格投影 1188 | gdf_fishgrid.crs = wrf_proj.crs 1189 | # 输出模型网格shp文件 1190 | gdf_fishgrid.to_file('output/shapefile-grid.shp', 1191 | driver='ESRI Shapefile', 1192 | encoding='utf-8') 1193 | 1194 | 1195 | # 基于MEIC清单的source文件构建 1196 | def create_source(label, year, mm, development_list, emission_factor_dir, emission_data_dir, out_path): 1197 | # import pandas as pd 1198 | import glob 1199 | import tqdm 1200 | 1201 | for development in development_list: 1202 | emission_factor_file = f'{emission_factor_dir}/EF_{development}.csv' 1203 | if os.path.exists(f"{out_path}/source-{label}-{development}-{year}-{mm}.csv"): 1204 | continue 1205 | 1206 | # 读取排放因子文件 1207 | ef_data = pd.read_csv(emission_factor_file) 1208 | 1209 | result = pd.DataFrame(columns=["LON", "LAT"]) 1210 | result['LON'] = ef_data['LON'] 1211 | result['LAT'] = ef_data['LAT'] 1212 | 1213 | files = glob.glob(f'{emission_data_dir}/{label}_{year}_{mm}__{development}__*.csv') 1214 | 1215 | for file in tqdm.tqdm(files, desc=f'Create source file of {development}'): 1216 | specie = encode_title(os.path.basename(file))["pollutant"] 1217 | data = pd.read_csv(file) 1218 | values = [] 1219 | for i in range(ef_data.shape[0]): 1220 | temp_area = ef_data['AREA'].values[i] 1221 | temp_ef = ef_data['EF'].values[i] 1222 | temp_big_emis = data[data['NAME'].isin([temp_area])]['SUM'].values 1223 | temp_small_emis = temp_ef * temp_big_emis 1224 | try: 1225 | values.append(temp_small_emis[0]) 1226 | except IndexError: 1227 | values.append(0.0) 1228 | result[specie] = values 1229 | 1230 | result.to_csv(f"{out_path}/source-{label}-{development}-{year}-{mm}.csv", index=False) 1231 | 1232 | 1233 | def encode_title(file_name): 1234 | import re 1235 | # Get the species name from file name. 1236 | condition = f"(.*?)_(.*?)_(.*?)__(.*?)__(.*?).csv" 1237 | encode_name = re.findall(condition, file_name)[0] 1238 | label = encode_name[0] 1239 | year = encode_name[1] 1240 | month = encode_name[2] 1241 | sector = encode_name[3] 1242 | pollutant = encode_name[4] 1243 | dict = {"label": label, 1244 | "year": year, 1245 | "month": month, 1246 | "sector": sector, 1247 | "pollutant": pollutant} 1248 | return dict 1249 | 1250 | 1251 | def encode_title_tiff(file_name): 1252 | import re 1253 | # Get the species name from file name. 1254 | condition = f"(.*?)_(.*?)_(.*?)__(.*?)__(.*?).tiff" 1255 | encode_name = re.findall(condition, file_name)[0] 1256 | label = encode_name[0] 1257 | year = encode_name[1] 1258 | month = encode_name[2] 1259 | sector = encode_name[3] 1260 | pollutant = encode_name[4] 1261 | dict = {"label": label, 1262 | "year": year, 1263 | "month": month, 1264 | "sector": sector, 1265 | "pollutant": pollutant} 1266 | return dict 1267 | 1268 | 1269 | def read_tiff(file): 1270 | import rioxarray as rxr 1271 | import numpy as np 1272 | """ 1273 | 1274 | :param file: The file path of GeoTIFF. 1275 | :return: value, longitude, latitude. 1276 | """ 1277 | dataset = rxr.open_rasterio(file) 1278 | longitude = dataset.coords["x"].values 1279 | latitude = dataset.coords["y"].values 1280 | lons, lats = np.meshgrid(longitude, latitude) 1281 | value = dataset[0, ...].values 1282 | return value, lons, lats 1283 | 1284 | 1285 | def create_source_table(input_dir, mm, sector): 1286 | import glob 1287 | # import pandas as pd 1288 | import re 1289 | """ 1290 | 1291 | :param input_dir: the path of input directory which store the GeoTIFF files. 1292 | :param mm: the month of emission file date. 1293 | :param sector: the sector of emission file. 1294 | :return: DataFrame, a table include latitude, longitude and species values. 1295 | """ 1296 | # Search the files in the input directory. 1297 | files = glob.glob(f"{input_dir}/*_{mm}__{sector}__*.tiff") 1298 | 1299 | # Build a DataFrame to store the inventory information. 1300 | df = pd.DataFrame(columns=["lat", "lon"]) 1301 | # --- Start loop of the sector files. 1302 | for file in files: 1303 | # Read the GeoTIFF and return the value and geographic information. 1304 | value, lons, lats = read_tiff(file) 1305 | 1306 | # Get the species name from file name. 1307 | basename = os.path.basename(file) 1308 | condition = f"(.*?)_(.*?)_{mm}__{sector}__(.*?).tiff" 1309 | encode_name = re.findall(condition, basename)[0] 1310 | # label = encode_name[0] 1311 | # year = encode_name[1] 1312 | pollutant = encode_name[2] 1313 | # Put the pollutant to pd.DataFrame. 1314 | df[pollutant] = value.flatten() 1315 | df["lat"] = lats.flatten() 1316 | df["lon"] = lons.flatten() 1317 | return df 1318 | 1319 | 1320 | def source2cmaq( 1321 | emission_date, 1322 | grid_desc, 1323 | grid_name, 1324 | sector, 1325 | input_dir, 1326 | inventory_mechanism, 1327 | target_mechanism, 1328 | output_dir, 1329 | ): 1330 | """ 1331 | 1332 | :param emission_date: set the date for CMAQ emission file. 1333 | :param grid_desc: the path of GRIDDESC file. 1334 | :param grid_name: set the grid name. 1335 | :param sector: the sector of original emission inventory. 1336 | :param input_dir: the path of input directory which store the GeoTIFF files. 1337 | :param inventory_mechanism: set chemical mechanism of inventory. 1338 | :param target_mechanism: set chemical mechanism of CMAQ emission file. 1339 | :param output_dir: set the output directory. 1340 | :return: 1341 | """ 1342 | # import pandas as pd 1343 | import datetime 1344 | import PseudoNetCDF as pnc 1345 | import numpy as np 1346 | 1347 | # Convert the emission date to other format. 1348 | datetime_emission = pd.to_datetime(emission_date) 1349 | yyyymmdd = datetime.datetime.strftime(datetime_emission, "%Y%m%d") 1350 | # yyyy = datetime.datetime.strftime(datetime_emission, "%Y") 1351 | mm = datetime.datetime.strftime(datetime_emission, "%m") 1352 | # dd = datetime.datetime.strftime(datetime_emission, "%d") 1353 | yyyyjjj = datetime.datetime.strftime(datetime_emission, "%Y%j") 1354 | w = datetime.datetime.strftime(datetime_emission, "%w") 1355 | 1356 | # Create template file. 1357 | gf = pnc.pncopen( 1358 | grid_desc, 1359 | GDNAM=grid_name, 1360 | format="griddesc", 1361 | SDATE=int(yyyyjjj), 1362 | TSTEP=10000, 1363 | withcf=False, 1364 | ) 1365 | gf.updatetflag(overwrite=True) 1366 | tmpf = gf.sliceDimensions(TSTEP=[0] * 25) 1367 | max_col_index = getattr(tmpf, "NCOLS") - 1 1368 | max_row_index = getattr(tmpf, "NROWS") - 1 1369 | 1370 | # Create the source file and read it. 1371 | data = create_source_table(input_dir, mm, sector) 1372 | 1373 | # Add I and J coordinate and calculate the total emission. 1374 | data["I"], data["J"] = tmpf.ll2ij(data.lon.values, data.lat.values) 1375 | celltotal = data.groupby(["I", "J"], as_index=False).sum() 1376 | celltotal = celltotal[ 1377 | (celltotal["I"] >= 0) 1378 | & (celltotal["J"] >= 0) 1379 | & (celltotal["I"] <= max_col_index) 1380 | & (celltotal["J"] <= max_row_index) 1381 | ] 1382 | celltotal = celltotal.drop(columns=["lon", "lat"], axis=1) 1383 | 1384 | # Read species file. 1385 | species_file = ( 1386 | f"species/{inventory_mechanism}_{target_mechanism}_speciate_{sector}.csv" 1387 | ) 1388 | species_info = pd.read_csv(species_file) 1389 | fname_list = species_info.pollutant.values 1390 | var_list = species_info.emission_species.values 1391 | factor_list = species_info.split_factor.values 1392 | divisor_list = species_info.divisor.values 1393 | origin_units = species_info.inv_unit.values 1394 | target_units = species_info.emi_unit.values 1395 | 1396 | # Read the temporal file. 1397 | # _monthly_factor = pd.read_csv("temporal/monthly.csv") 1398 | _weekly_factor = pd.read_csv("temporal/weekly.csv") 1399 | _hourly_factor = pd.read_csv("temporal/hourly.csv") 1400 | # monthly_factor = _monthly_factor[sector].values 1401 | weekly_factor = _weekly_factor[sector].values 1402 | hourly_factor = _hourly_factor[sector].values 1403 | 1404 | # Loop the species and create the variable to IOAPI file. 1405 | items = zip( 1406 | fname_list, var_list, factor_list, divisor_list, origin_units, target_units 1407 | ) 1408 | for fname, var, split_factor, divisor, origin_unit, target_unit in items: 1409 | try: 1410 | # Extract the current pollutant. 1411 | df = celltotal[["I", "J", fname]] 1412 | 1413 | # Convert monthly emission to weekly emission. 1414 | weekly_values = df[fname].values * 0.25 1415 | 1416 | # Convert weekly emission to daily emission. 1417 | daily_values = weekly_values * weekly_factor[int(w)] 1418 | 1419 | # Convert daily emission to hourly emission. 1420 | df_list = [] 1421 | for hour_i in range(24): 1422 | _df = pd.DataFrame(columns=["J", "I", "hour", "values"]) 1423 | _df["J"] = df.J.values 1424 | _df["I"] = df.I.values 1425 | _df["hour"] = np.zeros(df.shape[0]) + hour_i 1426 | _df["values"] = daily_values * hourly_factor[hour_i] 1427 | df_list.append(_df) 1428 | result = pd.concat(df_list) 1429 | 1430 | # Convert original units to target units and input the split_factor. 1431 | if origin_unit == "Mmol" and target_unit == "mol/s": 1432 | result["values"] = result["values"] * 1000000.0 / 3600.0 * split_factor 1433 | elif origin_unit == "Mg" and target_unit == "g/s": 1434 | result["values"] = result["values"] * 1000000.0 / 3600.0 * split_factor 1435 | elif origin_unit == "Mg" and target_unit == "mol/s": 1436 | result["values"] = ( 1437 | result["values"] * 1000000.0 / 3600.0 / divisor * split_factor 1438 | ) 1439 | 1440 | # Convert the I, J, hour to int. 1441 | result[["hour", "J", "I"]] = result[["hour", "J", "I"]].astype("int") 1442 | h = result.hour 1443 | i = result.I 1444 | j = result.J 1445 | 1446 | # Create the variable of emission. 1447 | evar = tmpf.createVariable(var, "f", ("TSTEP", "LAY", "ROW", "COL")) 1448 | if target_unit == "mol/s": 1449 | evar.setncatts(dict(units="moles/s", long_name=var, var_desc=var)) 1450 | elif target_unit == "g/s": 1451 | evar.setncatts(dict(units="g/s", long_name=var, var_desc=var)) 1452 | evar[h, h * 0, j, i] = result["values"].values 1453 | 1454 | except KeyError: 1455 | # If don not have this pollutant in GeoTIFF, skip it. 1456 | print(f"Warning: Do not have the pollutant named {fname}.") 1457 | continue 1458 | # Get rid of initial DUMMY variable 1459 | del tmpf.variables["DUMMY"] 1460 | 1461 | # Update TFLAG to be consistent with variables 1462 | tmpf.updatetflag(tstep=10000, overwrite=True) 1463 | 1464 | # Remove VAR-LIST so that it can be inferred 1465 | delattr(tmpf, "VAR-LIST") 1466 | tmpf.updatemeta() 1467 | 1468 | # Save out. 1469 | # output_name = f"{output_dir}/{grid_name}_{yyyymmdd}_{target_mechanism}_{sector}.nc" # 1.4 1470 | output_name = f"{output_dir}/{target_mechanism}_{sector}_{grid_name}_{yyyymmdd}.nc" # 1.5 1471 | tmpf.save(output_name, format="NETCDF3_CLASSIC") 1472 | tmpf.close() 1473 | # print(f"Finish: {output_name}") 1474 | 1475 | 1476 | def read_nml(nml_path): 1477 | import f90nml 1478 | example = f90nml.read(nml_path) 1479 | griddesc_file = example["global"]["griddesc_file"] 1480 | griddesc_name = example["global"]["griddesc_name"] 1481 | big_grid_file = example["global"]["big_grid_file"] 1482 | inventory_year = example["global"]["inventory_year"] 1483 | sectors = example["global"]["sectors"] 1484 | geotiff_dir = example["global"]["geotiff_dir"] 1485 | allocation_raster = example["global"]["allocator"] 1486 | inventory_label = example["global"]["inventory_label"] 1487 | inventory_mechanism = example["global"]["inventory_mechanism"] 1488 | target_mechanism = example["global"]["target_mechanism"] 1489 | start_date = example["global"]["start_date"] 1490 | end_date = example["global"]["end_date"] 1491 | cores = example["global"]["cores"] 1492 | return griddesc_file, griddesc_name,\ 1493 | big_grid_file, inventory_year,\ 1494 | sectors, geotiff_dir, allocation_raster, inventory_label, inventory_mechanism, target_mechanism,\ 1495 | start_date, end_date, cores 1496 | 1497 | 1498 | def main_f2c(): 1499 | # -------------------------------------------------------------------------------------------------------- 1500 | if user_control() is True: 1501 | print("### This system is developed by Haofan Wang. ###") 1502 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 1503 | else: 1504 | print("### This system is developed by Haofan Wang. ###") 1505 | print("### You can contact me for any suggestions. ###") 1506 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 1507 | print("### *************************************************** ###") 1508 | print("### The current version has expired. ###") 1509 | print("### Please contact me to request the latest version. ###") 1510 | print("### *************************************************** ###") 1511 | return 1512 | # -------------------------------------------------------------------------------------------------------- 1513 | import f90nml 1514 | import multiprocessing 1515 | # -------------------------------------------------------------------------------------------------------- 1516 | example = f90nml.read("namelist.input") 1517 | gridFile = example["global"]["griddesc_file"] 1518 | gridName = example["global"]["griddesc_name"] 1519 | 1520 | # The path of input directory which store the GeoTIFF files. 1521 | input_directory = example["global"]["geotiff_dir"] 1522 | 1523 | # Set the sectors of these emission inventory. 1524 | sectors = example["global"]["sectors"] 1525 | if type(sectors) != type(["list"]): 1526 | sectors = [sectors] 1527 | 1528 | # Set the date for CMAQ emission file. 1529 | start_date = pd.to_datetime(example["global"]["start_date"]) 1530 | end_date = pd.to_datetime(example["global"]["end_date"]) 1531 | 1532 | # Set chemical mechanism. This parameter is set for species file. 1533 | inventory_mechanism = example["global"]["inventory_mechanism"] 1534 | target_mechanism = example["global"]["target_mechanism"] 1535 | 1536 | # Set the output directory. 1537 | path = os.getcwd() 1538 | output_directory = f"{path}/output" 1539 | 1540 | # Set the parallel cores. 1541 | cores = example["global"]["cores"] 1542 | 1543 | # Make output directory. 1544 | if not os.path.exists(output_directory): 1545 | os.makedirs(output_directory) 1546 | 1547 | # Obtain the available core number. 1548 | num_cores = multiprocessing.cpu_count() 1549 | print("The total core: ", num_cores) 1550 | print("Your set is: ", cores) 1551 | if cores > num_cores: 1552 | print("Please ensure that the number of cores used " 1553 | "does not exceed the maximum number of cores on your computer.") 1554 | exit() 1555 | 1556 | # Create a threading pool that use the all cores. 1557 | pool = multiprocessing.Pool(cores) 1558 | 1559 | # Build a argument pool. 1560 | # import pandas as pd 1561 | emission_dates = [str(date) for date in pd.period_range(pd.to_datetime(start_date), pd.to_datetime(end_date))] 1562 | arg_pool = [] 1563 | for emisfile_date in emission_dates: 1564 | for sector in sectors: 1565 | arg = (emisfile_date, gridFile, gridName, sector, input_directory, 1566 | inventory_mechanism, target_mechanism, output_directory) 1567 | arg_pool.append(arg) 1568 | 1569 | # Start cores. 1570 | results = pool.starmap(source2cmaq, arg_pool) 1571 | 1572 | # Close the thread pool. 1573 | pool.close() 1574 | pool.join() 1575 | 1576 | print("Done") 1577 | 1578 | 1579 | def read_allocator(nml_path): 1580 | import f90nml 1581 | example = f90nml.read(nml_path) 1582 | allocator_types = example["global"]["allocator_type"] 1583 | return allocator_types 1584 | 1585 | 1586 | def read_line(nml_path): 1587 | import f90nml 1588 | example = f90nml.read(nml_path) 1589 | line_files = example["line"]["line_files"] 1590 | line_factors = example["line"]["line_factors"] 1591 | return line_files, line_factors 1592 | 1593 | 1594 | def main_createCMAQ(): 1595 | # -------------------------------------------------------------------------------------------------------- 1596 | if user_control() is True: 1597 | print("### This system is developed by Haofan Wang. ###") 1598 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 1599 | else: 1600 | print("### This system is developed by Haofan Wang. ###") 1601 | print("### You can contact me for any suggestions. ###") 1602 | print("### Email: wanghf58@mail2.sysu.edu.cn ###") 1603 | print("### *************************************************** ###") 1604 | print("### The current version has expired. ###") 1605 | print("### Please contact me to request the latest version. ###") 1606 | print("### *************************************************** ###") 1607 | return 1608 | # -------------------------------------------------------------------------------------------------------- 1609 | import multiprocessing 1610 | # -------------------------------------------------------------------------------------------------------- 1611 | griddesc_file, griddesc_name, big_grid_file, inventory_year, sectors, geotiff_dir, allocation_raster, \ 1612 | inventory_label, inventory_mechanism, target_mechanism, start_date, end_date, cores = read_nml("namelist.input") 1613 | 1614 | if type(sectors) != type(["list"]): 1615 | _sectors = [] 1616 | _sectors.append(sectors) 1617 | sectors = _sectors 1618 | 1619 | # Obtain the available core number. 1620 | num_cores = multiprocessing.cpu_count() 1621 | print("The total core: ", num_cores) 1622 | print("Your set is: ", cores) 1623 | if cores > num_cores: 1624 | print("Please ensure that the number of cores used " 1625 | "does not exceed the maximum number of cores on your computer.") 1626 | exit() 1627 | 1628 | # Create a threading pool that use the all cores. 1629 | pool = multiprocessing.Pool(cores) 1630 | 1631 | # Build a argument pool. 1632 | emission_dates = [str(date) for date in pd.period_range(pd.to_datetime(start_date), pd.to_datetime(end_date))] 1633 | arg_pool = [] 1634 | for emisfile_date in emission_dates: 1635 | for sector in sectors: 1636 | arg = ( 1637 | emisfile_date, griddesc_file, griddesc_name, inventory_label, sector, inventory_year, inventory_mechanism, 1638 | target_mechanism) 1639 | 1640 | arg_pool.append(arg) 1641 | 1642 | # Start cores. 1643 | results = pool.starmap(create_emission_file, arg_pool) 1644 | 1645 | # Close the thread pool. 1646 | pool.close() 1647 | pool.join() 1648 | 1649 | print("Done") 1650 | --------------------------------------------------------------------------------