Jinyu He1, Wenbo Xue This email address is being protected from spambots. You need JavaScript enabled to view it.1, Li Yan This email address is being protected from spambots. You need JavaScript enabled to view it.2, Xurong Shi1, Yanchao Wang1,3, Yu Lei1, Yixuan Zheng1, Yu Zhang4 

1 Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China
Center for Beijing-Tianjin-Hebei Regional Ecology and Environment, Chinese Academy of Environmental Planning, Beijing 100012, China
3 College of New Energy and Environment, Jilin University, Jilin 130021, China
4 College of Chemistry, Zhengzhou University, Henan 450001, China


Received: December 9, 2021
Revised: April 8, 2022
Accepted: April 8, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.210254  

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Cite this article:

He, J., Xue, W., Yan, L., Shi, X., Wang, Y., Lei, Y., Zheng, Y., Zhang, Y. (2022). Multi-dimensional Sources Apportionment of PM2.5 in Zhongyuan Urban Agglomeration Based on the CAMx Mode. Aerosol Air Qual. Res. 22, 210254. https://doi.org/10.4209/aaqr.210254


HIGHLIGHTS

  • A two-dimensional cross matrix was established for cities and major emission sectors.
  • For average concentration, the transmission from urban agglomerations contributed 6.2%–26.3%.
  • Industrial sources contributed the most to the annual average concentration of PM2.5.
  • Household source emissions contributed the most in winter.
 

ABSTRACT


Based on the comprehensive air quality model with extensions (CAMx), the quantitative simulation was conducted on the transport impact of PM2.5 between Zhongyuan Urban Agglomeration and surrounding cities in 2017, and the contribution of 5 major emission sectors to urban PM2.5 concentrations, namely the sector of power, industry, household, transportation and agriculture. A two-dimensional cross matrix was established for 18 cities and major emission sectors in Zhongyuan Urban Agglomeration (hereafter referred as the Urban Agglomeration). The results showed that: On an annual average scale, among the transport contribution to the 18 cities of the Urban Agglomeration, 37.8%–57.8% was from local sources, 6.2%–26.3% from the transport within the Urban Agglomeration, and 5.9%–17.4% from other cities nearby; In terms of sectors contribution, industrial sources contributed the most to the annual average concentration of PM2.5 (12.7%–33.0%) in the Urban Agglomeration, followed by household, transportation and agriculture sources. Household source emissions contributed the most in winter. PM2.5 concentrations in Shangqiu, Puyang and Zhoukou affected by household sources emissions exceeding 30% in winter.


Keywords: PM2.5, CAMx, Zhongyuan Urban Agglomeration, Region-industry cross matrix


1 INTRODUCTION


Zhongyuan Urban Agglomeration (in Central Plains of China) is one of the 7 major urban agglomerations in China, with Henan Province as the main body. It has become one of the regions with the most serious PM2.5 pollutions in China, which located in the central area with high emission value of air pollution in the country (Li et al., 2017; Cai et al., 2017). Henan Province had the highest annual PM2.5 concentrations in China and PM2.5 pollution had a great impact on health due to the large number of people exposed to high concentrations. The pollution was often contributed by air pollutants from different regions and sectors, and showed significant regional pollution characteristics. Some studies conducted in recent years have shown that in the annual time scale, over 30% of the PM2.5 pollutions in some provinces and cities in eastern China was from external inputs, and some even exceeded 50% (Streets et al., 2007; Wu et al., 2013; Wang et al., 2016a). Therefore, it is urgent to study the transboundary transport characteristics of PM2.5 and clarify the transport contribution of pollutants emitted from cities and sectors, in order to effectively control PM2.5 pollutions in the Urban Agglomeration.

Chemical transport model is an important tool to assist in understanding the pattern of long-distance transport of PM2.5 (U.S. EPA, 2018; Downey et al., 2015; Ge et al., 2014). Some studies have been conducted on the source apportionment of PM2.5 use models in China. Xue et al. (2014) used CAMx-PSAT model to quantitatively simulate the cross-regional transport pattern of PM2.5 and its chemical components in China. Based on CAMx-PSAT air quality model, Wang et al. (2017) conducted a quantitative simulation of PM2.5 pollution and mutual transport characteristics in Beijing-Tianjin-Hebei region in 2015; Lu et al. (2015) discussed the impact of long-distance pollution transport on the air quality of Pearl River Delta based on Community Multiscale Air Quality Modelling System (CMAQ); Wang et al. (2005) used CalPuff model to simulate the air pollution transport in the urban agglomeration of Pearl River Delta region, and the research results showed that the pollution interaction among cities in the region was significant. However, most of the existing studies have done source apportionment based on space, and mainly focused on JJJ, Yangtze River Delta and Pearl River Delta area. Few has been done on the pattern of cross-border transport of PM2.5 in the Urban Agglomeration, the source apportionment on the dimensional cross of space-sector is especially lacking. It is still difficult to provide effective technical support for the joint prevention and control of regional air pollution in the Urban Agglomeration.

In this study, emission data of 2017 and air quality numerical model CAMx were used to simulate and analyse the impact of PM2.5 transports between the Urban Agglomeration and its surrounding cities, and to simulate the contribution of 5 major emission sectors (power, industry, household, transportation, agriculture) to urban PM2.5 concentrations. On this basis, the orthogonal decomposition study was carried out to establish a two-dimensional cross matrix between cities and major emission sectors to evaluate the influence of pollutants emitted by major sectors in different cities on urban PM2.5 concentrations through long-distance transport, in order to provide support for effective control of regional air pollution.

 
2 METHODS


 
2.1 Overview of the Study Area

Zhongyuan Urban Agglomeration covers the whole area of Henan Province, located at latitude 31°23'–36°22'N and longitude 110°21'–116°39'E, in the middle and lower reaches of the Yellow River in the southern part of the North China Plain. The regional resident population is 99,366,000, accounting for 7% of the national population, and it is an important grain producing area in China, accounting for 1/10th of the country's grain production in 2020 (NBS, 2021). The 18 cities in the area, Zhengzhou, Kaifeng, Luoyang, Pingdingshan, Anyang, Hebi, Xinxiang, Jiaozuo, Puyang, Xuchang, Luohe, Sanmenxia, Nanyang, Zhoukou, Zhumadian and Jiyuan, account for about 2.1% and 5.4% of the national SO2 and NOx emissions (MEE, 2022), The average PM2.5 concentrations of the Urban Agglomeration was 52 µg m3 in 2020 (Department of Ecology and Environment of Henan Province, 2021), which is the most polluted region for PM2.5 in China.

 
2.2 Air Quality Models and Models Settings

The air quality numerical model CAMX synthesizes various technical characteristics required by a "scientific" air quality model into a single system, which can comprehensively simulate gaseous and granular air pollutants at various scales, such as cities and regions (Xue et al., 2013). CAMX model features include bidirectional nesting and elastic nesting, grid plume (PIG) module, ozone source allocation technology (OSAT), particulate matter source tracking technology (PSAT), direct splitting algorithm (DDM) for sensitivity to ozone and other material sources, etc. (ENVIRON, 2013).

As a comprehensive method for sensitivity analysis and process analysis coupled in CAMX model, PSAT can obtain the production and elimination information of PM2.5 and its precursors in a tracer way. The contribution of different regions, different source categories, and boundary condition (BC) and initial condition (IC) to PM2.5 pollutions can be calculated. Its core function is to simulate the response relationship between pollution sources and environmental receptors.

Simulation area: The Lambert projection coordinate system was adopted with the central longitude of 113.6°E and central latitude of 34°N. The two parallel standard latitudes were 28°N and 40°N, respectively. Two layers of grids were nested. The horizontal resolution of the first grid was 18 km, covering whole China, the horizontal simulation range of the second grid was X direction (–354 to 318 km) and Y direction (–318 to 336 km), covering the whole Zhongyuan Urban Agglomeration, with a grid spacing of 6 km. The simulation area was divided into 112 × 109 grids (as shown in Fig. 1). A total of 14 air pressure layers were set in the vertical direction of the simulated region, and the spacing between layers increased gradually bottom up.

Fig. 1. Simulation are and cities in Zhongyuan urban agglomeration.
Fig. 1. Simulation are and cities in Zhongyuan urban agglomeration.

Simulation period: January, April, July and October of 2017, with January representing winter.

Meteorological parameters: The meteorological field required by the CAMx model was provided with the mesoscale meteorological Weather Research & Forecasting Model (WRF). The same spatial projection coordinate system and grid nesting rules were adopted for WRF and CAMx model, but the simulation range was larger than that of CAMx. The grid spacing of the second domain was 6 km, and the study area was divided into 114 × 111 grids. A total of 35 air pressure layers were arranged in the vertical direction with the spacing between the layers increasing gradually bottom up. The Final Operational Global Analysis (FNL) data (NCAR, 2017) provided by the National Center for Environmental Forecasting (NCEP) with the frequency of 6 h each time and 1° resolutions was adopted for the initial and boundary field data of WRF model. The parameterization scheme of WRF and parameter settings of CAMx model are shown in Table 1 and Table 2.

Table 1. WRF Parameterization scheme.

Table 2. CAMx model parameter settings.

 
2.3 Source Classification and Selection of Receptor Sites

Classification of pollution sources: According to the urban administrative classification, the Urban Agglomeration and its surrounding cities were divided into 31 subregions. The other regions in the simulated area were divided into one sub region, with a total of 32 regions. On this basis, 32 subregions were further classified based on the 5 emission source categories of power, industry, household, transportation and agriculture, resulted in the total of 32 × 5 = 160 pollution sources classified.

Selection of receptor sites: A total of 80 state-controlled air quality monitoring stations in 18 cities including Zhengzhou, Kaifeng and Luoyang under the jurisdiction of Henan Province were taken as recipient points, and the simulated data of recipient points in each city was calculated as the average value to represent the PM2.5 concentration of the city.

 
2.4 Emission Inventory

The chemical species in the emission inventory required by the CAMx model mainly include SO2, NOx, PM, NH3, VOCs and other pollutants. Emission data was based on the Multi-resolution emission inventory for China (MEIC) (Tsinghua University, 2017; Zheng et al., 2018), and the emission data for biogenic VOCs was derived from the global emission inventory ECCAD (2013).

 
2.5 Model Verification

Based on available Surface meteorological data for the cities of Zhengzhou, Anyang and Nanyang (NCDC, 2022), the hourly simulated values were compared with the observed values. The results show that the simulated wind speed and temperature are in good agreement with the observations, and they all have correlation coefficients greater than 0.65. The verification results are shown in Fig. 2.

Fig. 2. Comparison of simulated values wind speed and temperature with the observations.
Fig. 2. Comparison of simulated values wind speed and temperature with the observations.

The PM2.5 concentration data for the same period from environmental monitoring stations was used to verify the accuracy of the simulation results. Comparing the annual average PM2.5 concentrations and the average concentration in January simulated by CAMx model with the monitoring data, and calculating the correlations of annual mean for all cities. the results showed that the simulated values were well correlated with the monitored values, and the correlation coefficient R was 0.71 and 0.66 respectively. The verification results are shown in Fig. 3.

 Fig. 3.Comparison of simulated values with the monitored values.
Fig. 3.Comparison of simulated values with the monitored values.


3 RESULTS AND DISCUSSION


 
3.1 Characteristics of Inter-city Transport


3.1.1 Transport characteristics throughout the year

The PM2.5 pollution transport matrix of the Urban Agglomeration in 2017 was obtained based on CAMx-PSAT model simulation as shown in Table 3. On the annual average scale, the transport contribution of 37.8%–57.8% was from the local sources of 18 cities in Zhongyuan Urban Agglomeration, 6.2%–26.3% from the cities within the Agglomeration, and 5.9%–17.4% from other cities nearby.

Table 3. PM2.5 pollution transport matrix between cities in Zhongyuan Urban Agglomeration.

The most significant local source contribution to PM2.5 concentrations was from Luoyang and Zhengzhou, with the rate of 57.8% and 56.5% respectively. In addition, Luoyang was mainly affected by neighboring cities such as Zhengzhou and Jiaozuo, the total contribution of 19.9% was from the transport within the urban agglomeration, and 5.9% from other surrounding cities; In Zhengzhou, the contribution of 18.2% was from the transport within the Urban Agglomeration including the cities such as Xinxiang and Xuchang, and 6.0% from other surrounding cities; In Xinxiang, Nanyang, Zhoukou and Zhumadian, the contribution of more than 50% was from the local sources, 10.6%–18.9% from the transport within the Urban Agglomeration, and 7.5%–10.7% from other surrounding cities.

For the PM2.5 concentrations in other cities, over 50% was contributed by the transport. Among them, the transport contribution from Zhengzhou, Luoyang and Yuncheng to Sanmenxia was significant, and the total transport contribution from the Urban Agglomeration and its surrounding cities was 21.4%, while the local contribution was 37.8%, relatively small. Jiyuan, Luohe, Pingdingshan, Hebi, Xuchang and Jiaozuo were significantly affected by the transport within the Urban Agglomeration, with the rate of more than 20%. Anyang, Puyang and Shangqiu were affected largely from other surrounding cities such as Handan, Heze, Liaocheng, Bozhou and Jining, and the total transport contribution of other surrounding cities exceeded 14%, which was greater than that within the Urban Agglomeration. In addition, some cities located at the boundary of the simulated area, such as Shangqiu, Xinyang, Zhumadian and Puyang, are significantly contributed by the boundary condition (BC).

 
3.1.2 Transport characteristics in winter

Winter is the season with the heaviest PM2.5 pollutions. January is selected as the typical month to analyze the transport pattern in winter. Fig. 4 demonstrates the contribution of local sources was more than 50% in Zhengzhou, Luoyang, Xinxiang, Xuchang, Zhoukou and Zhumadian in winter. Zhengzhou and Luoyang were most significantly affected, with 56.4% and 56.0% respectively. The transport contribution to the PM2.5 concentrations in 7 cities such as Pingdingshan, Hebi, Jiaozuo, Luohe, Sanmenxia, Nanyang and Xinyang, was greater in winter than that in other seasons throughout the year. Among them, Sanmenxia, Hebi and Luohe were most affected. The transport contribution to the PM2.5 concentrations by Yuncheng, Anyang, Zhoukou and other cities was significant in winter.

Fig. 4. Transport Contribution to PM2.5 in winter in different cities.
Fig. 4. Transport Contribution to PM2.5 in winter in different cities.

 
3.2 Impact of Cities on PM2.5 Concentrations in the Urban Agglomeration

Each city not only receives PM2.5 input from other cities within the region as a "receptor", but also transmits PM2.5 to other cities within the region as an "emission source". However, due to the influence of geographical location, meteorological conditions, pollution emission intensity and distribution, cities have different influences on PM2.5 pollution of other cities in the region. The contribution of each city to the average PM2.5 concentrations of the other 17 cities in the Urban Agglomeration was added up to calculate the outward transport contribution (output). Fig. 5 shows that Zhengzhou had the largest outward transport contribution, exceeding 30 µg m3, which contributed more than 2 µg m3 to the average PM2.5 concentrations in Luoyang, Hebi, Xinxiang, Jiaozuo, Sanmenxia, Jiyuan and other cities; Xinxiang, Xuchang, Zhoukou and Jiaozuo were the second largest, with the outward transport contribution of more than 15 µg m3. The contribution of each city to the annual average PM2.5 concentrations in the Urban Agglomeration are shown in Fig. 6.

Fig. 5. Analysis on the input and output of PM2.5 in cities.
Fig. 5. Analysis on the input and output of PM2.5 in cities.

Fig. 6. Influence of cities on the annual average PM2.5 concentrations in Zhongyuan Urban Agglomeration.
Fig. 6. Influence of cities on the annual average PM2.5 concentrations in Zhongyuan Urban Agglomeration.

 
3.3 Sector Contribution


3.3.1 Annual contribution

The annual average PM2.5 concentrations in each city contributed by 5 categories of emission sources such as the sector of power, industry, household, transportation and agriculture in the Urban Agglomeration and its surrounding cities, was obtained based on the simulation using CAMx-PSAT model, as shown in Fig. 7. In general, industrial emissions contributed the most to the annual average PM2.5 concentrations in all cities (16.2%–37.7%), the contribution of industrial emissions to PM2.5 concentrations in Zhengzhou, Luoyang, Jiyuan, Xuchang and Jiaozuo was more than 30%. The contribution of household source emissions to annual average PM2.5 concentrations in each city was second only to industrial sources, with a contribution rate of 13.6%–25.3%. The contribution of household source emissions to PM2.5 concentrations in Shangqiu, Zhoukou and Puyang was significant, nearly 25%. The contribution of transportation source emissions to the annual average PM2.5 concentrations in each city was 9.9%–13.3%, Zhengzhou, Zhoukou and Nanyang were greatly affected by the source with the rate of about 13%. As a major agricultural province, agricultural sources also contributed significantly to the annual average PM2.5 concentrations in cities (8.1%–15.3%). The contribution of agricultural sources to PM2.5 concentrations in Zhoukou, Luohe and Nanyang was about 15%. The contribution of power emission to the annual average PM2.5 concentrations in each city was about 1.9%–11.4%, and Jiyuan was most significantly affected (11.4%).

Fig. 7. Transport contribution of major emission sectors to PM2.5 concentrations.
Fig. 7. Transport contribution of major emission sectors to PM2.5 concentrations.

 
3.3.2 Contribution to winter concentrations

Fig. 8 demonstrates the emissions from household sources made the largest contribution to PM2.5 concentrations in winter, with the rate ranging from 23.4% to 40.7%, followed by industrial sources with the rate ranging from 10.5% to 29.1%. The contribution of the industry to different cities varied greatly, The PM2.5 concentrations in Shangqiu, Zhoukou, Puyang and Zhumadian in winter by the contribution of household sources was over 37%, which was higher than the sum of the other 4 categories of emission sources; Besides household sources, the contribution of industrial sources and transportation sources was also significant in Zhengzhou and Luoyang. The contribution of industrial sources and transportation sources was 29.1% and 10.0% respectively to Zhengzhou; 26.9% and 9.0% respectively to Luoyang in winter; The contribution of agricultural sources to Luohe, Xinyang and Nanyang exceeded 12%, second only to household sources and industrial sources; The contribution of power emission to Jiyuan was greater (11%), second only to household and industrial sources.

Fig. 8. Transport contribution of major emission sectors to PM2.5 concentrations in winter.
Fig. 8. Transport contribution of major emission sectors to PM2.5 concentrations in winter.

 
3.4 Cross-contribution of Space-Sector in Typical Cities

The PM2.5 transport matrix showed that the mutual transport between cities such as Zhengzhou, Kaifeng, Luoyang, Pingdingshan, Xinxiang, Jiaozuo, Xuchang, Luohe, Zhoukou, Jiyuan was significant. The above-mentioned typical cities were selected for the analysis on the cross-contribution of city-industry, as shown in Fig. 9. The transport contribution from the Urban Agglomeration and the nearby cities to the PM2.5 concentrations in Zhengzhou was 23.9%. The ranking of the cross-contribution of space-sector in descending order was as follows: Xuchang industrial sources (1.6%), Xinxiang industrial sources (1.4%), household sources (1.0%), agricultural sources (0.7%) and Kaifeng industrial sources (0.7%) etc. The contribution of Urban Agglomeration and its surrounding cities to Kaifeng was 28.9%, the ranking of the space-sector emission sources in descending order was as follows: Zhengzhou industrial sources (1.6%), Xuchang industrial sources (1.4%), Xinxiang household sources (1.0%) and agricultural sources (0.8%), etc. The PM2.5 concentrations in Luoyang contributed by the transport from Urban Agglomeration and its surrounding cities was 25.4%, among which the industrial sources from Zhengzhou contributed the most, 3.4%; followed by the industrial sources in Jiaozuo (1.5%), transportation sources in Zhengzhou (1.3%), and household sources in Zhengzhou (0.7%) etc. Overall, Zhengzhou had the greatest influence on Luoyang.

Fig. 9. Cross contribution of space-sector to the PM2.5 concentrations in typical cities.
Fig. 9. Cross contribution of space-sector to the PM2.5 concentrations in typical cities.

The transport contribution of the Urban Agglomeration and its surrounding cities to Xinxiang, located in the northern part of Henan Province, was 26.4%. Zhengzhou industrial sources contributed the most to the PM2.5 concentrations in Xinxiang (3.2%), followed by Zhengzhou transportation sources (1.1%), Handan industrial sources (1.1%), Xuchang industrial sources (0.8%) and Jiaozuo industrial sources (0.8%). The transport contribution of Urban Agglomeration and its surrounding cities to Jiaozuo was 31.2%. The ranking of the contribution in descending order was as follows: Zhengzhou industrial sources (3.0%), Xinxiang industrial sources (2.3%), Jincheng industrial sources (1.6%), Xinxiang household sources (1.3%), Zhengzhou transportation sources (1.3%), etc. The transport contribution of Urban Agglomeration and its surrounding cities to Jiyuan was 35.2%. The contribution of Jiaozuo industrial sources to the PM2.5 concentrations was the largest (4.8%), followed by Zhengzhou industrial sources (2.6%), Jiaozuo household sources (1.9%), agricultural sources (1.6%), transportation sources (1.4%), and Zhengzhou transportation sources (1.2%).

The transport contribution of Urban Agglomeration and its surrounding cities to Pingdingshan, located in the central part of Henan Province, was 28.7%. The ranking of the contribution in descending order was as follows: Xuchang industrial sources (2.4%), household sources (1.6%), Zhengzhou industrial sources (1.4%), Xuchang agricultural sources (1.3%), and Luohe industrial sources (0.9%), etc. Xuchang made the largest contribution. The transport contribution of Urban Agglomeration and its surrounding cities to Xuchang was 27.3%. Luohe industrial sources had the highest contribution (2.0%), followed by Zhengzhou industrial sources (1.2%), Luohe household sources (1.3%), Kaifeng industrial sources (1.1%), Luohe transportation sources (0.8%), and Pingdingshan industrial sources (0.8%), etc. The transport contribution of the Urban Agglomeration and its surrounding cities to the PM2.5 concentrations in Luohe was 30.1%. The ranking of the contribution in descending order was as follows: Zhumadian household sources (2.0%), Zhoukou household sources (1.9%), Zhoukou industrial sources (1.4%), Zhumadian industrial sources (1.4%), transportation sources (1.3%) and agricultural sources (1.2%) etc. Zhumadian and Zhoukou had a significant influence on Luohe.

 
3.5 Discussion

The PM2.5 pollution transport matrix in the Urban Agglomeration simulated based on CAMx-PSAT model showed that the transport between the cities in the Urban Agglomeration was significant, especially in the area of Zhengzhou-Luoyang-Xinxiang-Jiaozuo-Jiyuan and Kaifeng-Pingdingshan-Xuchang-Luohe-Zhoukou-Zhumadian, which should be the focus of regional joint prevention and control. The transport contribution of Zhengzhou, Xinxiang, Xuchang, Zhoukou, Jiaozuo etc. was significant to the cities within the Urban Agglomeration. Looking from the point of space-sector cross contribution, the contribution was larger from Zhengzhou industrial sources, Zhengzhou transportation sources, Xuchang industry sources, Xuchang household sources, Xinxiang industry sources, Xinxiang household sources, Xinxiang agricultural sources, Zhoukou household sources, Zhoukou industry sources, Jiaozuo industry sources, Jiaozuo household sources etc. Strengthening the management of the related industries in the above-mentioned cities is very important to improve regional PM2.5 pollution control.

By sectors, the industrial sources had the greatest impact on annual average concentration of PM2.5, followed by household sources, transportation sources and agriculture sources. Compared with the study on the simulation of sources in 2013–2014 (Wang et al., 2016b), the contribution of household sources to the PM2.5 concentrations was reduced to a certain extent, but it was still the largest in winter, and the higher contribution in winter may be related to heating. Cities should continue to strengthen emissions control from industrial and household sources, especially in winter, while controlling emissions from transportation and agriculture.

In addition, due to the large uncertainty of the emission inventory of fugitive dust sources and the large error in the analysis results using the air quality model, the contribution of the fugitive dust was not considered in this study. Since the fugitive dust was mainly from local sources, the contribution of the local sources was underestimated in this study. The results of the study on urban source apportionment showed that fugitive dust was an important source of PM2.5 in the Urban Agglomeration. The contribution rate of fugitive dust sources to the PM2.5 concentrations was 8.2% in the urban area, and 12.4% to that in the suburban area of Zhengzhou (Zhang et al., 2020). The contribution rate of road and construction fugitive dust to the PM2.5 concentrations in the winter of 2016 in Xinxiang was 13.1% (Yan et al., 2019). The contribution rate of soil sources in mining area to PM2.5 in Pingdingshan in summer was 11.6% (Liu et al., 2020). While strengthening regional joint prevention and control, cities should continue to strengthen local fugitive dust control to reduce PM2.5 concentrations.

 
4 CONCLUSIONS


On the annual average scale, the transport contribution to the 18 cities of Urban Agglomeration was 37.8%–57.8% from local sources, 6.2%–26.3% from the cities within the Urban Agglomeration, and 5.9%–17.4% from other surrounding cities. The contribution of local sources from Luoyang, Zhengzhou, Xinxiang, Nanyang, Zhoukou and Zhumadian was more than 50%. The transport contribution to Jiyuan, Luohe, Pingdingshan, Hebi, Xuchang and Jiaozuo was significant from other cities within the Urban Agglomeration, with the rate of more than 20%. The PM2.5 concentrations in 7 cities, i.e., Pingdingshan, Hebi, Jiaozuo, Luohe, Sanmenxia, Nanyang and Xinyang was more affected in winter by the transport contribution than that in other seasons throughout the year. Among them, Sanmenxia, Hebi and Xinyang were the most affected by the contribution.

In terms of sectors, industrial emissions contributed the most to the annual average PM2.5 concentrations (12.7%–33.0%) in the Urban Agglomeration, followed by household sources (12.0%–20.0%) and transportation sources (9.1%–12.1%). The contribution of agricultural sources was also significant, 7.8%–11.0%, to the annual average PM2.5 concentrations in each city. In winter, household source emissions contributed the most to PM2.5 concentrations in each city, with the rate of 19.6%–32.4%, the contribution to Shangqiu, Puyang and Zhoukou was more than 30%.

Among the cities in the Urban Agglomeration, Zhengzhou had the largest outward transport flux of PM2.5, exceeding 30 µg m3; Followed by Xinxiang, Xuchang, Zhoukou and Jiaozuo, all exceeding 15 µg m3. From the perspective of space-industry cross-contribution, the transport contribution of Zhengzhou industrial and transportation sources, Xuchang industrial and household sources, Xinxiang industrial, household and agricultural sources, Zhoukou household and industrial sources, Jiaozuo industrial and household sources contributed more to the surrounding cities, with the rate ranging from 0.8% to 4.8%.

 
ACKNOWLEDGMENTS


The financial support by: The National Natural Science Foundation [grant numbers 72174126].

 
DISCLAIMER


The authors confirm that there are no conflicts of interest.


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