Atmospheric pollutant transportation influence assessment method coupling natural and trade perspectives

Abstract

The invention provides an atmospheric pollutant transportation influence assessment method coupled with natural and trade perspectives, and relates to the technical field of atmospheric environment governance, and the method comprises the steps: constructing an input-output model of environment expansion, calculating the consumption and input-based atmospheric pollutant discharge amount, the unit energy emission intensity and the implicit pollutant discharge amount of consumption transfer and supply transfer between the regions of each region; carrying out return calculation on the discharge amount of the hidden pollutants; constructing a meteorological and air quality mode simulation scheme, and redistributing the returned atmospheric pollutant emissions of each region based on the production, consumption and supply perspectives into the grids; and the pollutant conveying influence of a supply-production-consumption whole chain is simulated. According to the method, quantitative evaluation of the influence of atmospheric pollutant transportation under the perspective of a full supply chain of supply-production-consumption is realized, differences caused by geographical meteorology, energy and industrial structures and trade activities among regions are considered, and targeted support is provided for optimization of regional joint defense and joint control strategies of collaborative development of environment and economy.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric environmental governance technology, and in particular to an atmospheric pollution transport impact assessment method coupling nature and trade perspectives. Background Art

[0002] Air pollution exhibits significant complex and regional characteristics. Regional air quality is influenced not only by local emission sources but also by the transport of pollutants from surrounding areas. Furthermore, with the rapid development of economic globalization and regional economic integration, regional trade is becoming increasingly frequent, shifting product production from consumption areas to production areas, and with it, associated air pollutant emissions. The implicit emission shifts driven by trade result in significant differences in emission distribution between the supply and consumption sides compared to the production side, impacting regional air quality and public health.

[0003] Combining numerical simulation with a multi-regional input-output model to illustrate the impact of inter-regional atmospheric pollution transport from the perspective of nature and trade is of great significance for formulating coordinated regional pollution prevention and control strategies and achieving coordinated development of the environment and economy.

[0004] At present, research on the impact of regional atmospheric pollution transport is mainly based on production-end emissions, ignoring the impact of economic activity factors on pollutant transport, and lacks an assessment method that couples the joint effects of natural and trade factors. Summary of the Invention

[0005] In response to the problems in the background technology, the present invention provides an atmospheric pollution transport impact assessment method that couples the natural and trade perspectives. By coupling the input-output model and the air quality model, it comprehensively considers the impact of trade-implied emission transfer and atmospheric chemical transmission, and studies the impact of economic trade and natural processes of atmospheric pollution on regional atmospheric pollutant transport and the air quality concentration of atmospheric pollutants, providing a scientific basis for policy formulation and pollution control of relevant departments.

[0006] To achieve the above objectives, the present invention provides an atmospheric pollutant transport impact assessment method that couples natural and trade perspectives, including:

[0007] Based on the input-output tables and pollutant emission data of each region within the scope of the assessment, an environmental expansion input-output model is constructed to calculate the atmospheric pollutant emissions of each region based on consumption and input, and the implicit pollutant emission transfers between regions due to consumption demand transfers and supply-driven transfers.

[0008] Calculate the unit energy emission intensity of each region based on the energy consumption data of each region and the atmospheric pollutant emissions based on production in each region;

[0009] Based on the unit energy emission intensity of each region, the implicit pollutant emission transfer amount of each region's consumption demand outward transfer and supply-driven inward transfer is returned and calculated, and combined with the atmospheric pollutant emissions based on production in each region, the atmospheric pollutant emissions after return from the consumption perspective and the supply perspective are obtained;

[0010] Construct a meteorological and air quality model simulation scheme suitable for the area to be assessed, divide the area to be assessed into grids, and mark emission source areas;

[0011] Match each region's production-based atmospheric pollutant emissions to the grid; combine the region's population density data to reallocate each region's returned atmospheric pollutant emissions based on consumption and supply perspectives to the grid;

[0012] The source tracing module of the meteorological and air quality model simulation scheme is used to simulate the impact of pollutant transport on the entire supply-production-consumption chain, and obtain the results of the regional atmospheric pollutant transport impact from the perspectives of nature and trade.

[0013] As a further improvement of the present invention, the total input and total output data of each region and the intermediate input data between regions are obtained based on the input-output table of each region, the direct consumption coefficient and the distribution coefficient are calculated, and then combined with the identity matrix I to obtain the Leontief inverse matrix L and the Ghosh inverse matrix G. The formula is:

[0014]

[0015] L=(IA) -1

[0016]

[0017] G=(IB) -1

[0018] Where:

[0019] a rs is the direct consumption coefficient, where area r is the intermediate product directly consumed in producing one monetary unit of output value in area s;

[0020] b rS is the allocation coefficient, which represents the intermediate product directly used by region s to produce one monetary unit of output value of region r;

[0021] z rs is the intermediate input from region r to region s;

[0022] A is the direct consumption coefficient matrix;

[0023] B is the distribution coefficient matrix;

[0024] xs is the total output of region s;

[0025] x r is the total output of region r;

[0026] Based on the total output data of each region and combined with the atmospheric pollutant emission data of each region, the emission intensity coefficient of each region is calculated using the following formula:

[0027]

[0028] Where:

[0029] f r is the atmospheric pollutant emission intensity coefficient of region r;

[0030] e r is the production-based atmospheric pollutant emissions in region r;

[0031] Based on the input-output table of each region, the final demand data y and initial input data v of each region are obtained, and the input-output model of environmental extension is constructed based on the regional atmospheric pollutant emission intensity coefficient f, Leontief inverse matrix L and Ghosh inverse matrix G. The formula is:

[0032]

[0033] in,

[0034] E is the emission of atmospheric pollutants based on consumption;

[0035] E′ is the emission of atmospheric pollutants based on input.

[0036] As a further improvement of the present invention, the implicit pollutant emission transfer amount of the consumption demand transfer and supply-driven transfer between regions is calculated using the formula:

[0037]

[0038] Where:

[0039] E r is the amount of air pollutant emissions in region r based on the consumption perspective;

[0040] E rr is the local air pollutant emissions caused by local consumption in region r;

[0041] E rs is the amount of air pollutant emissions in region r caused by the consumption demand in region s;

[0042] E ′r is the atmospheric pollutant emissions in region r based on the input perspective;

[0043] E′ rr is the local air pollutant emissions driven by local supply in region r;

[0044] E′ rs Emissions of air pollutants in region s driven by supply to region r.

[0045] As a further improvement of the present invention, based on the energy consumption data of each region and the atmospheric pollutant emissions based on production in each region, the unit energy emission intensity of each region is calculated using the formula:

[0046]

[0047] Where:

[0048] UE r is the unit energy emission intensity of region r;

[0049] e r is the production-based atmospheric pollutant emissions in region r;

[0050] W r is the energy consumption in region r.

[0051] As a further improvement of the present invention, the calculation formula for the returned atmospheric pollutant emissions in each region based on the consumption perspective and the supply perspective is:

[0052]

[0053] Where:

[0054] M s is the air pollutant emissions after refund based on consumption perspective in region s;

[0055] E ss is the amount of air pollutant emissions caused by local consumption in region s; The amount of air pollutant emissions transferred from the consumption demand of region s to region r is returned to region s based on the unit energy emission intensity of region s;

[0056] M′ s is the air pollutant emissions after return based on the input perspective in region s;

[0057] E′ ss is the amount of atmospheric pollutant emissions from local inputs in region s;

[0058] The atmospheric pollutant emissions driven by the supply of region r to region s are returned to the atmospheric pollutant emissions generated in region s according to the unit energy emission intensity of region s.

[0059] As a further improvement of the present invention, a meteorological and air quality model simulation scheme applicable to the scope to be assessed is constructed, including an emission source area labeling scheme and a gridded emission inventory resolution and grid number scheme;

[0060] Gridding the area to be assessed based on the gridded emissions inventory resolution, grid number scheme, and simulation computing power;

[0061] Mark the emission source areas according to the emission source area marking scheme and the boundary lines of each area;

[0062] As a further improvement of the present invention,

[0063] Create a fishnet of target resolution based on the Create Fishnet tool;

[0064] Obtain the production-based gridded emission inventory for each region, and match the atmospheric pollutant emissions of each grid in the production-based gridded emission inventory to the fishing network.

[0065] As a further improvement of the present invention,

[0066] Combined with the population density data of each region, the returned atmospheric pollutant emissions from each region based on the consumption perspective and the supply perspective are redistributed to the grid; including:

[0067] Download the population density data using the spatial join tool and re-match it to the fishnet;

[0068] The air pollutant emissions after the return from the consumption perspective and the supply perspective of each region are redistributed to the grids of each region according to the population density, and the grid emission inventory based on consumption return and the grid emission inventory based on input return are obtained. The formula is:

[0069]

[0070] Where:

[0071] E i is the atmospheric pollutant emissions of grid i after returning based on the consumption perspective;

[0072] M s is the air pollutant emissions after refund based on consumption perspective in region s;

[0073] E′ i is the atmospheric pollutant emission of grid i after returning based on the input perspective;

[0074] M ′s is the air pollutant emissions after return based on the input perspective in region s;

[0075] P i is the population in grid i, Ps is the total population of area s;

[0076] As a further improvement of the present invention, the annual average atmospheric pollutant emission data in the fishing net is averaged into daily average atmospheric pollutant emission data, so that the time resolution of the atmospheric pollutant emission data is consistent with that of the meteorological data.

[0077] As a further improvement of the present invention, a meteorological and air quality model simulation scheme applicable to the range to be assessed is constructed, which also includes a meteorological parameterization scheme and an air quality model chemical mechanism scheme;

[0078] Based on the meteorological parameterization scheme and the chemical mechanism scheme of the air quality model, the production-based gridded emission inventory, the consumption-based rebate-based gridded emission inventory, and the input-rebate-based gridded emission inventory are input into the meteorological and air quality models. With the ERA5 reanalysis dataset as the meteorological driver, the source tracing module of the air quality model is applied to simulate the changes in atmospheric pollutant concentrations under the supply-production-consumption scenarios, as well as the mutual influence contribution of pollutants between emission source areas, to obtain the results of the mutual transport impact of atmospheric pollutants between regions from the coupled natural and trade perspectives.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] Based on the input-output tables of each region in the study area, the atmospheric pollutant emission inventory, population density data and numerical simulation technology, the present invention realizes the quantitative assessment of the impact of atmospheric pollution transportation from the perspective of the entire supply chain of supply-production-consumption, taking into account the differences in geographical meteorology, energy and industrial structure, and trade activities among regions, and solves the limitations of traditional methods that only focus on production-end emissions or a single physical transmission path, providing targeted support for the optimization of regional joint prevention and control strategies for the coordinated development of environment and economy.

[0081] This invention comprehensively considers the impact of atmospheric physical and chemical processes and trade activities on the transport of atmospheric pollutants, realizes a systematic assessment of the impact of pollutant transport on air quality from different perspectives, provides technical support for the formulation of regional pollution coordinated prevention and control strategies, and supports the coordinated development of regional environment and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of a method for assessing the impact of atmospheric pollutant transport from a coupled natural and trade perspective, disclosed in one embodiment of the present invention;

[0083] Figure 2 The PM data of Beijing, Tianjin, Hebei and the surrounding four provinces in 2017 disclosed in one embodiment of the present invention 2.5 Schematic diagram of unit energy emission intensity;

[0084] Figure 3The PM after the production end, consumption end and supply end in 2017 in Beijing, Tianjin, Hebei and surrounding provinces is disclosed in an embodiment of the present invention. 2.5 Emission comparison diagram;

[0085] Figure 4 This is the supply-side, production-side, and consumption-side PM data for Beijing, Tianjin, Hebei, and the surrounding four provinces in July 2017, disclosed in an embodiment of the present invention. 2.5 Impact Matrix. DETAILED DESCRIPTION

[0086] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0087] The present invention is described in further detail below with reference to the accompanying drawings:

[0088] like Figure 1 As shown, the method for assessing the impact of atmospheric pollutant transport from the coupling nature and trade perspectives provided by the present invention includes the following steps:

[0089] S1. Build an environmentally extended input-output model based on the input-output tables and pollutant emission data of each region within the scope of the assessment, calculate the atmospheric pollutant emissions of each region based on consumption and input, and calculate the implicit pollutant emission transfers between regions due to consumption demand transfers and supply-driven transfers;

[0090] in,

[0091] Based on the input-output table of each region, we obtain the total input and total output data of each region, as well as the intermediate input data between regions, calculate the direct consumption coefficient and the distribution coefficient, and then combine the unit matrix I to obtain the Leontief inverse matrix L and the Ghosh inverse matrix G. The formula is:

[0092]

[0093] L=(IA) -1

[0094]

[0095] G=(IB) -1

[0096] Where:

[0097] a rsis the direct consumption coefficient, where area r is the intermediate product directly consumed in producing one monetary unit of output value in area s;

[0098] b rs is the allocation coefficient, which represents the intermediate product directly used by region s to produce one monetary unit of output value of region r;

[0099] z rs is the intermediate input from region r to region s;

[0100] A is the direct consumption coefficient matrix;

[0101] B is the distribution coefficient matrix;

[0102] x s is the total output of region s;

[0103] x r is the total output of region r;

[0104] Based on the total output data of each region and combined with the atmospheric pollutant emission data of each region, the emission intensity coefficient of each region is calculated using the following formula:

[0105]

[0106] Where:

[0107] f r is the atmospheric pollutant emission intensity coefficient of region r;

[0108] e r is the production-based atmospheric pollutant emissions in region r;

[0109] Based on the input-output table of each region, the final demand data y and initial input data v of each region are obtained, and the input-output model of environmental extension is constructed based on the regional atmospheric pollutant emission intensity coefficient f, Leontief inverse matrix L and Ghosh inverse matrix G. The formula is:

[0110]

[0111] in,

[0112] E is the emission of atmospheric pollutants based on consumption;

[0113] E′ is the emission of atmospheric pollutants based on input.

[0114] Further,

[0115] The implied pollutant emission transfers caused by the shift in consumption demand and supply-driven transfers between regions are calculated using the following formula:

[0116]

[0117] Where:

[0118] E r is the amount of air pollutant emissions in region r based on the consumption perspective;

[0119] E rr is the local air pollutant emissions caused by local consumption in region r;

[0120] E rs is the amount of air pollutant emissions in region r caused by the consumption demand in region s;

[0121] E ′r is the atmospheric pollutant emissions in region r based on the input perspective;

[0122] E ′rr is the local air pollutant emissions driven by local supply in region r;

[0123] E′ rs Emissions of air pollutants in region s driven by supply to region r.

[0124] S2. Calculate the unit energy emission intensity of each region based on the energy consumption data of each region and the atmospheric pollutant emissions based on production in each region;

[0125] The formula for calculating the unit energy emission intensity of each region is:

[0126]

[0127] Where:

[0128] UE r is the unit energy emission intensity of region r;

[0129] e r is the production-based atmospheric pollutant emissions in region r;

[0130] W r is the energy consumption in region r.

[0131] S3. Based on the unit energy emission intensity of each region, the implicit pollutant emission transfer amount of each region due to the outward transfer of consumption demand and the inward transfer driven by supply is returned and calculated. Combined with the atmospheric pollutant emissions based on production in each region, the atmospheric pollutant emissions after return from the consumption perspective and the supply perspective are obtained.

[0132] The calculation formula for the returned atmospheric pollutant emissions of each region based on the consumption perspective and the supply perspective is as follows:

[0133]

[0134] Where:

[0135] M s is the air pollutant emissions after refund based on consumption perspective in region s;

[0136] E ss is the amount of air pollutant emissions caused by local consumption in region s; The amount of air pollutant emissions transferred from the consumption demand of region s to region r is returned to region s based on the unit energy emission intensity of region s;

[0137] M′ s is the air pollutant emissions after return based on the input perspective in region s;

[0138] E′ ss is the amount of atmospheric pollutant emissions from local inputs in region s;

[0139] The atmospheric pollutant emissions driven by the supply of region r to region s are returned to the atmospheric pollutant emissions generated in region s according to the unit energy emission intensity of region s.

[0140] S4. Construct a meteorological and air quality model simulation scheme suitable for the area to be assessed, divide the area into grids, and mark emission source areas;

[0141] in,

[0142] Construct meteorological and air quality model simulation schemes suitable for the scope to be assessed, including emission source area labeling schemes and gridded emission inventory resolution and grid number schemes;

[0143] Grid division of the assessment area based on the gridded emission inventory resolution and grid number scheme and simulation computing power;

[0144] Mark emission source areas according to the emission source area marking plan and the boundaries of each area;

[0145] S5. Match each region's production-based atmospheric pollutant emissions to the grid; combine the population density data of each region and reallocate each region's returned atmospheric pollutant emissions based on the consumption and supply perspectives to the grid;

[0146] in,

[0147] Create a fishing net of target resolution using the Create Fishnet tool. For example, use the Create Fishnet tool in ArcGIS to create a fishing net of target resolution, and then use the Spatial Join tool to re-match the emissions of each grid in the downloaded production-side gridded emissions inventory to the target fishing net.

[0148] Obtain the production-based gridded emission inventory for each region, and match the atmospheric pollutant emissions of each grid in the production-based gridded emission inventory to the fishing network.

[0149] Furthermore, combined with the population density data of each region, the returned atmospheric pollutant emissions from each region based on the consumption perspective and the supply perspective are redistributed to the grid; including:

[0150] Download the population density data through the spatial join tool and re-match it to the fishing network. For example, use the "Spatial Join" tool in ArcGIS software to re-match the population density data to the target fishing network to make the spatial resolution consistent.

[0151] The returned atmospheric pollutant emissions from the consumption perspective and the supply perspective of each region are redistributed to the grids of each region according to population density, and the gridded emission inventory based on consumption return and the gridded emission inventory based on input return are obtained and matched to the fishing network. The formula is:

[0152]

[0153] Where:

[0154] E i is the atmospheric pollutant emissions of grid i after returning based on the consumption perspective;

[0155] M s is the air pollutant emissions after refund based on consumption perspective in region s;

[0156] E′ i is the atmospheric pollutant emission of grid i after returning based on the input perspective;

[0157] M′ s is the air pollutant emissions after return based on the input perspective in region s;

[0158] P i is the population in grid i, P s is the total population of area s;

[0159] Furthermore,

[0160] The annual average air pollutant emission data in the fishing net were averaged into daily average air pollutant emission data to make the time resolution of air pollutant emission data consistent with that of meteorological data.

[0161] S6. Apply the source tracing module of the meteorological and air quality model simulation program to simulate the impact of pollutant transport along the entire supply-production-consumption chain, and obtain the results of regional atmospheric pollutant transport impacts from both natural and trade perspectives.

[0162] Among them, the construction of meteorological and air quality model simulation schemes suitable for the scope to be assessed also includes meteorological parameterization schemes and air quality model chemical mechanism schemes;

[0163] Based on the meteorological parameterization scheme and the chemical mechanism scheme of the air quality model, the production-based gridded emission inventory, the consumption-based rebate-based gridded emission inventory, and the input-rebate-based gridded emission inventory were input into the meteorological and air quality models. With the ERA5 reanalysis dataset as the meteorological driver, the source tracing module of the air quality model was applied to simulate the changes in atmospheric pollutant concentrations under various supply-production-consumption scenarios, as well as the mutual influence contribution of pollutants between emission source areas, to obtain the results of the mutual transport impact of atmospheric pollutants between regions from the coupled natural and trade perspectives.

[0164] Example:

[0165] The present invention provides an atmospheric pollutant transport impact assessment method that couples the perspectives of nature and trade. The study area is Beijing-Tianjin-Hebei and four surrounding provinces (Shanxi Province, Inner Mongolia Autonomous Region, Shandong Province, and Henan Province). 2.5 Conduct a 2017 regional air pollution transport impact assessment for target air pollutants from a natural and trade perspective, such as Figure 1 As shown, the following steps are included:

[0166] Step 1: Obtain the multi-regional input-output table of 31 provinces in China in 2017 from the CEADs database, and obtain economic data such as total output (x), intermediate input (z), initial input (v), and final demand (y) of the Beijing-Tianjin-Hebei region and the four surrounding provinces; obtain PM of the Beijing-Tianjin-Hebei region and the four surrounding provinces in 2017 from the MEIC database. 2.5 Emissions (e) are divided by total output to obtain the emission intensity coefficient f. The emission intensity coefficient is combined with the Leontief inverse matrix and final demand data to obtain the consumption-based air pollutant emissions E of the Beijing-Tianjin-Hebei region and the four surrounding provinces. The emission intensity coefficient is combined with the Ghosh inverse matrix and initial input data to obtain the input-based air pollutant emissions E′ of the Beijing-Tianjin-Hebei region and the four surrounding provinces:

[0167]

[0168] Step 2: Emissions from consumption in region r (E r ) is the direct emission from local consumption (E rr ) and other provinces in the study area and 24 other provinces in China, resulting in pollutant emissions in the r region The sum of the consumption-end emissions in region r (E r ) minus local emissions (E rr), we can get the implicit pollution transfer from the perspective of consumption in the region; the supply-side emissions in region r (E′ r ) is the direct emission of local input (E′ rr ) and other provinces in the study area and 24 other provinces in China. The sum of the supply-side emissions of region r (E′ r ) minus local emissions (E′ rr ), we can get the implicit pollution transfer amount from the perspective of the region’s investment:

[0169]

[0170] Step 3: Obtain the energy consumption data of Beijing-Tianjin-Hebei and the four surrounding provinces in 2017 from the China Energy Statistical Yearbook (W r ), combined with PM in the MEIC database 2.5 Emissions The unit energy emission intensity (UE r ), which reflects the PM generated per unit of energy consumption 2.5 Emissions. Figure 2 The energy intensity per unit of energy in Beijing, Tianjin, Hebei and the four surrounding provinces in 2017 is shown. Based on this indicator, the pollutant emissions (E rs The energy consumed is The amount of energy consumed is calculated based on the unit energy emission intensity (UE s UE t ) to obtain the emissions generated by the consumption of these energies in the consumption area, that is, the emissions after return from the consumption perspective of area s (M s ); Similarly, according to the above calculation method, the emissions transferred from the supply province to the production province are returned to the supply province according to the unit energy emission intensity of the supply province, and the returned emissions from the supply perspective of region s are obtained (M′ s ),like Figure 3 As shown:

[0171]

[0172]

[0173] Step 4: Simulation scheme and emission labeling: Based on the administrative boundaries of provinces, municipalities and autonomous regions in my country, the emission source areas of Beijing-Tianjin-Hebei and the four surrounding provinces are labeled.

[0174] Step 5: Prepare a gridded inventory. Use the “Create Fishnet” tool in ArcGIS to create a fishing net with a spatial resolution of 36 km. Obtain a gridded emission inventory at the production end with a spatial resolution of 0.25°*0.25° from the MEIC database. Use the “Spatial Join” tool to match the emission of each grid in the downloaded gridded emission inventory to the target fishing net. Obtain the 2017 national population density data with a spatial resolution of 1 km from the WorldPop database. Use the “Spatial Join” tool in ArcGIS to match the population density data to the fishing net with a spatial resolution of 36 km. s , M′ s ) According to the population ratio of each grid Assign it to each grid to generate a 36km gridded emission inventory. Then, according to the monthly emission ratio of the production-end emission inventory, the annual average emissions of the returned emission inventory are averaged into monthly average emissions, and then averaged into daily average emissions to make it consistent with the time resolution of the meteorological data:

[0175]

[0176] Step 6: Apply the SA module of the WRF-CAMx model, combine the gridded emission inventories of the supply side, production side, and consumption side in July 2017 prepared in S5 and the ERA5 meteorological data in July, and simulate the PM2.5 in the Beijing-Tianjin-Hebei region and the surrounding four provinces under three scenarios. 2.5 The interaction matrix, such as Figure 4 As shown, the comprehensive impact of regional air pollution transport from the perspective of nature and trade is obtained.

[0177] Advantages of the present invention:

[0178] Based on the input-output tables of each region in the study area, the atmospheric pollutant emission inventory, population density data and numerical simulation technology, the present invention realizes the quantitative assessment of the impact of atmospheric pollution transportation from the perspective of the entire supply chain of supply-production-consumption, taking into account the differences in geographical meteorology, energy and industrial structure, and trade activities among regions, and solves the limitations of traditional methods that only focus on production-end emissions or a single physical transmission path, providing targeted support for the optimization of regional joint prevention and control strategies for the coordinated development of environment and economy.

[0179] This invention comprehensively considers the impact of atmospheric physical and chemical processes and trade activities on the transport of atmospheric pollutants, realizes a systematic assessment of the impact of pollutant transport on air quality from different perspectives, provides technical support for the formulation of regional pollution coordinated prevention and control strategies, and supports the coordinated development of regional environment and economy.

[0180] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for assessing the impact of atmospheric pollutant transport by coupling the natural and trade perspectives, characterized in that: include: Based on the input-output tables and pollutant emission data of each region within the scope of the assessment, an environmental expansion input-output model is constructed to calculate the atmospheric pollutant emissions of each region based on consumption and input, and the implicit pollutant emission transfers between regions due to consumption demand transfers and supply-driven transfers. Calculate the unit energy emission intensity of each region based on the energy consumption data of each region and the atmospheric pollutant emissions based on production in each region; Based on the unit energy emission intensity of each region, the implicit pollutant emission transfer amount of each region's consumption demand outward transfer and supply-driven inward transfer is returned and calculated, and combined with the atmospheric pollutant emissions based on production in each region, the atmospheric pollutant emissions after return from the consumption perspective and the supply perspective are obtained; Construct a meteorological and air quality model simulation scheme suitable for the area to be assessed, divide the area to be assessed into grids, and mark emission source areas; Match each region's production-based atmospheric pollutant emissions to the grid; combine each region's population density data to reallocate each region's returned atmospheric pollutant emissions based on consumption and supply perspectives to the grid; The source tracing module of the meteorological and air quality model simulation scheme is used to simulate the impact of pollutant transport on the entire supply-production-consumption chain, and obtain the results of the regional atmospheric pollutant transport impact from the perspectives of nature and trade.

2. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: Based on the input-output table of each region, we obtain the total input and total output data of each region, as well as the intermediate input data between regions, calculate the direct consumption coefficient and the distribution coefficient, and then combine the unit matrix I to obtain the Leontief inverse matrix L and the Ghosh inverse matrix G. The formula is: L=(I-A) -1 G=(I-B) -1 Where: a rs is the direct consumption coefficient, which represents the intermediate products directly consumed by region r to produce one monetary unit of output value of region s; b rs is the allocation coefficient, which represents the intermediate product directly used by region s to produce one monetary unit of output value of region r; z rs is the intermediate input from region r to region s; A is the direct consumption coefficient matrix; B is the distribution coefficient matrix; x s is the total output of region s; x r is the total output of region r; Based on the total output data of each region and combined with the atmospheric pollutant emission data of each region, the emission intensity coefficient of each region is calculated using the following formula: Where: f r is the atmospheric pollutant emission intensity coefficient of region r; e r is the production-based atmospheric pollutant emissions in region r; Based on the input-output table of each region, the final demand data y and initial input data v of each region are obtained, and the input-output model of environmental extension is constructed based on the regional atmospheric pollutant emission intensity coefficient f, Leontief inverse matrix L and Ghosh inverse matrix G. The formula is: in, E is the emission of atmospheric pollutants based on consumption; E ′ is the amount of air pollutant emissions based on input.

3. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: The implied pollutant emission transfers caused by the shift in consumption demand and supply-driven transfers between regions are calculated using the following formula: Where: E r is the amount of air pollutant emissions in region r based on the consumption perspective; E rr is the local air pollutant emissions caused by local consumption in region r; E rs is the amount of air pollutant emissions in region r caused by the consumption demand in region s; E ′r is the atmospheric pollutant emissions in region r based on the input perspective; E ′rr is the local air pollutant emissions driven by local supply in region r; E ′rs Emissions of air pollutants in region s driven by supply to region r.

4. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: Based on the energy consumption data of each region and the atmospheric pollutant emissions based on production in each region, the unit energy emission intensity of each region is calculated using the following formula: Where: UE r is the unit energy emission intensity of region r; e r is the production-based atmospheric pollutant emissions in region r; W r is the energy consumption in region r.

5. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: The calculation formula for the returned atmospheric pollutant emissions of each region based on the consumption perspective and the supply perspective is: Where: M s is the air pollutant emissions after refund based on consumption perspective in region s; E ss is the amount of air pollutant emissions caused by local consumption in region s; The amount of air pollutant emissions transferred from the consumption demand of region s to region r is returned to region s based on the unit energy emission intensity of region s; M′ s is the air pollutant emissions after return based on the input perspective in region s; E′ ss Emissions of air pollutants driven by local supply in region s; The atmospheric pollutant emissions driven by the supply of region r to region s are returned to the atmospheric pollutant emissions generated in region s according to the unit energy emission intensity of region s.

6. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: Construct meteorological and air quality model simulation schemes suitable for the scope to be assessed, including emission source area labeling schemes and gridded emission inventory resolution and grid number schemes; Gridding the area to be assessed based on the gridded emissions inventory resolution, grid number scheme, and simulation computing power; The emission source areas are marked according to the emission source area marking scheme and the boundary lines of each area.

7. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 1 is characterized by: Create a fishnet of target resolution based on the Create Fishnet tool; Obtain the production-based gridded emission inventory for each region, and match the atmospheric pollutant emissions of each grid in the production-based gridded emission inventory to the fishing network.

8. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 7 is characterized by: Combined with the population density data of each region, the returned atmospheric pollutant emissions from each region based on the consumption perspective and the supply perspective are redistributed to the grid; including: Download the population density data using the spatial join tool and re-match it to the fishnet; The air pollutant emissions after the return from the consumption perspective and the supply perspective of each region are redistributed to the grids of each region according to the population density, and the grid emission inventory based on consumption return and the grid emission inventory based on input return are obtained. The formula is: Where: E i is the atmospheric pollutant emissions of grid i after returning based on the consumption perspective; M s is the air pollutant emissions after refund based on consumption perspective in region s; E ′ i is the atmospheric pollutant emission of grid i after returning based on the input perspective; M ′s is the air pollutant emissions after return based on the input perspective in region s; P i is the population in grid i, P s is the total population of area s.

9. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 8 is characterized by: The annual average air pollutant emission data in the fishing net were averaged into daily average air pollutant emission data to make the time resolution of air pollutant emission data consistent with that of meteorological data.

10. The atmospheric pollutant transport impact assessment method coupling nature and trade perspectives according to claim 8 is characterized by: Construct meteorological and air quality model simulation schemes suitable for the scope to be assessed, including meteorological parameterization schemes and air quality model chemical mechanism schemes; Based on the meteorological parameterization scheme and the chemical mechanism scheme of the air quality model, the production-based gridded emission inventory, the consumption-based rebate-based gridded emission inventory, and the input-rebate-based gridded emission inventory are input into the meteorological and air quality models. With the ERA5 reanalysis dataset as the meteorological driver, the source tracing module of the air quality model is applied to simulate the changes in atmospheric pollutant concentrations under the supply-production-consumption scenarios, as well as the mutual influence contribution of pollutants between emission source areas, to obtain the results of the mutual transport impact of atmospheric pollutants between regions from the coupled natural and trade perspectives.

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