Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> compositions and sources during 2015–2019 at multisite of a Chinese megacity

Abstract. A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a fast-developing Chinese megacity as the studied area to investigate the spatiotemporal and policy-related variations of PM2.5 compositions and sources based on a long-term observation at multisite. A total of 836 samples were collected at 19 sites in wintertime of 2015–2019. According to the specific characteristics, 19 sampling sites were assigned into three layers. Layer 1 was the most urbanized area referred to the core zone of Chengdu, layer 2 was located in the outside circle of layer 1, and layer 3 belonged to the outer-most zone with the lowest urbanization level. The averaged PM2.5 concentrations for five years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121µg m−3). And for each year, the spatial clustering of chemical compositions at sampling sites was generally consistent with the classification of layers. PM2.5 compositions for layer 3 in 2019 were found to be similar to that for other layers two or three years ago, implying that the urbanization levels had a strong effect on air quality. During the sampled period, a decreasing trend was observed for the annual averaged PM2.5 concentrations, especially at sampling sites in layer 1, which was caused by the more strict control policies implemented in layer 1. The SO42−/NO3− mass ratio at most sites exceeded 1 in 2015 but dropped less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified with the average contributions of 15.5 % (traffic emission), 19.7 % (coal and biomass combustion), 8.8 % (industrial emission), 39.7 % (secondary particles) and 16.2 % (resuspended dust), respectively. From 2015 to 2019, dramatical decline was observed in the average percentage contributions of coal and biomass combustion, but traffic emission source showed an increasing trend. For spatial variations, coal and biomass combustion and industrial emission showed the stronger distribution patterns. High contributions of resuspended dust were occurred at sites with intensive construction activities such as subway and airport constructions. Combining the PMF results, we developed the source weighted potential source contribution function (SWPSCF) method for source localization, this new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was well-evaluated.

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Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-16219-2021 ◽

2021 ◽

Vol 21 (21) ◽

pp. 16219-16235

Author(s):

Xinyao Feng ◽

Yingze Tian ◽

Qianqian Xue ◽

Danlin Song ◽

Fengxia Huang ◽

...

Keyword(s):

Air Quality ◽

Spatial Clustering ◽

Sampling Period ◽

Temporal Variations ◽

Average Concentration ◽

Biomass Combustion ◽

Industrial Emissions ◽

Traffic Emission ◽

Average Percentage ◽

Layer 2

Abstract. A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM2.5 composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM2.5 concentrations for 5 years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121 µg m−3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM2.5 composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM2.5, especially at sampling sites in layer 1, where the stricter control policies were implemented. The SO42-/NO3- mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO42- and NO3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.

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A modeling analysis of a heavy air pollution episode occurred in Beijing

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-8215-2006 ◽

2006 ◽

Vol 6 (4) ◽

pp. 8215-8240 ◽

Cited By ~ 2

Author(s):

X. An ◽

T. Zhu ◽

Z. Wang ◽

C. Li ◽

Y. Wang

Keyword(s):

Air Pollution ◽

Air Quality ◽

Fine Particulate Matter ◽

Horizontal Resolution ◽

Transport Pathway ◽

Emissions Inventory ◽

Average Percentage ◽

Pollution Episode ◽

Transport Path ◽

Transport Flux

Abstract. Because concentrations of fine particulate matter (PM) and ozone in Beijing often exceed healthful levels, China is to taking steps to improve Beijing's air quality for the 2008 Olympic Games. In this paper the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System was used to investigate a heavy air pollution episode in Beijing during 3–7 April 2005. The modeling domain covered from East Asia with four nested grids with 81 to 3 km horizontal resolution focusing on urban Beijing. This was coupled with a regional emissions inventory with a 10 km resolution and a local 1km Beijing emissions database. The trend of predicted concentrations of various pollutants agreed reasonably well with the observations and captured the main features of this heavy pollution episode. The simulated column concentration distribution of PM was correlated reasonably with the MODIS remote sensing products. Control runs with and without Beijing emissions were conducted to quantify the contributions of non-Beijing sources (NBS) to the Beijing local air pollution. The contributions of NBS to each species differed spatially and temporally with the order of PM25>PM10>SO2>SOIL for this episode. The percentage contribution of NBS to fine particle (PM2.5) in Beijing was averaged about 40%, up to 80% at the northwest of urban Beijing and only 10–20% at southwest. The spatial distribution of NBS contributions for PM10 was similar to that for PM2.5, with a slightly less average percentage of about 30%. The NBS contributions for SO2 and SOIL (diameter between 2.5 μm and 10 μm) were only 10–20% and 5–10%. In addition, the pollutant transport flux was calculated and compared at different levels to investigate transport pathway and magnitude. It was found that the NBS contribution correlated with the transport flux, contributing 70% of PM10 concentration in Beijing at the time of transport flux peak during a strong episode with a transport path from southwest to northeast.

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A modeling analysis of a heavy air pollution episode occurred in Beijing

Atmospheric Chemistry and Physics ◽

10.5194/acp-7-3103-2007 ◽

2007 ◽

Vol 7 (12) ◽

pp. 3103-3114 ◽

Cited By ~ 106

Author(s):

X. An ◽

T. Zhu ◽

Z. Wang ◽

C. Li ◽

Y. Wang

Keyword(s):

Air Pollution ◽

Air Quality ◽

Fine Particulate Matter ◽

Horizontal Resolution ◽

Transport Pathway ◽

Emissions Inventory ◽

Average Percentage ◽

Pollution Episode ◽

Transport Path ◽

Transport Flux

Abstract. The concentrations of fine particulate matter (PM) and ozone in Beijing often exceed healthful levels in recent years, therefore China is to taking steps to improve Beijing's air quality for the 2008 Olympic Games. In this paper, the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System was used to investigate a heavy air pollution episode in Beijing during 3–7 April 2005 to obtain the basic information of how heavy air pollution formed and the contributions of local sources and surround emissions. The modeling domain covered from East Asia with four nested grids with 81 to 3 km horizontal resolution focusing on urban Beijing. This was coupled with a regional emissions inventory with a 10 km resolution and a local 1 km Beijing emissions database. The trend of predicted concentrations of various pollutants agreed reasonably well with the observations and captured the main features of this heavy pollution episode. The simulated column concentration distribution of PM was correlated well with the MODIS remote sensing products. Control runs with and without Beijing emissions were conducted to quantify the contributions of non-Beijing sources (NBS) to the Beijing local air pollution. The contributions of NBS to each species differed spatially and temporally with the order of PM2.5>PM10>SO2> soil for this episode. The percentage contribution of NBS to fine particle (PM2.5) in Beijing was averaged about 39%, up to 53% at the northwest of urban Beijing and only 15% at southwest. The spatial distribution of NBS contributions for PM10 was similar to that for PM2.5, with a slightly less average percentage of about 30%. The average NBS contributions for SO2 and soil (diameter between 2.5 μm and 10 μm) were 18% and 10%. In addition, the pollutant transport flux was calculated and compared at different levels to investigate transport pathway and magnitude. It was found that the NBS contribution correlated with the transport flux, contributing 60% of PM10 concentration in Beijing at the time of transport flux peak during a strong episode with a transport path from southwest to northeast.

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Contributions of Traffic and Industrial Emission Reductions to the Air Quality Improvement after the Lockdown of Wuhan and Neighboring Cities Due to COVID-19

Toxics ◽

10.3390/toxics9120358 ◽

2021 ◽

Vol 9 (12) ◽

pp. 358

Author(s):

Xiaoxiao Feng ◽

Xiaole Zhang ◽

Cenlin He ◽

Jing Wang

Keyword(s):

Air Quality ◽

Emission Reduction ◽

Manufacturing Industry ◽

Air Quality Model ◽

Quality Model ◽

Traffic Emission ◽

Public And Private ◽

Industrial Emission ◽

Pollutant Reduction ◽

Novel Coronavirus

Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

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Air Quality and Environmental Injustice in India: Connecting Particulate Pollution to Social Disadvantages

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010304 ◽

2021 ◽

Vol 18 (1) ◽

pp. 304

Author(s):

Jayajit Chakraborty ◽

Pratyusha Basu

Keyword(s):

Air Pollution ◽

Air Quality ◽

Transport Model ◽

Spatial Clustering ◽

Fine Particulate Matter ◽

Central India ◽

Chemical Transport Model ◽

Concentration Data ◽

Northern India ◽

Pm2.5 Concentration

While air pollution levels in India are amongst the highest in the world, the link between exposure to air pollution and social disadvantages has not been systematically examined. Using a distributive environmental justice framework, this study connects fine particulate matter (PM2.5) concentration data derived from satellite observations, a global chemical transport model, and ground-based measurements to district level socio-demographic information from the 2011 Census of India. The research objectives are to determine if annual average PM2.5 concentrations (2010) and recent increases in average PM2.5 concentrations (2010–2016) are unequally distributed with respect to socially disadvantaged population and household groups, after controlling for relevant contextual factors and spatial clustering. Overall, more than 85% of people and households in India reside in districts where international air quality standards for PM2.5 are exceeded. Although PM2.5 concentration levels are significantly higher in more urbanized districts located predominantly in northern India, recent increases have occurred in less urbanized areas located mainly in southern and central India. Multivariable statistical analysis indicated: (1) higher PM2.5 concentration in districts with higher percentages of Scheduled Castes (SCs), young children, and households in poor condition residence and without toilets; and (2) higher PM2.5 increases in less urbanized districts with higher percentages of SCs, females, children, people with disabilities, and households with no toilets. These findings thus highlight the need to consider the role of air pollution in exacerbating the consequences of social disadvantages in India.

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How villages contribute to their local air quality – the influence of traffic- and biomass combustion-related emissions assessed by mobile mappings of PM and its components

Atmospheric Environment ◽

10.1016/j.atmosenv.2021.118648 ◽

2021 ◽

pp. 118648

Author(s):

Friederike Fachinger ◽

Frank Drewnick ◽

Stephan Borrmann

Keyword(s):

Air Quality ◽

Biomass Combustion

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Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

Remote Sensing ◽

10.3390/rs13152981 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2981

Author(s):

Jeanné le Roux ◽

Sundar Christopher ◽

Manil Maskey

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Land Cover ◽

Spectral Response ◽

Fine Particulate Matter ◽

Daily Basis ◽

Land Cover Types ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference ◽

Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

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Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽

10.3390/atmos12030302 ◽

2021 ◽

Vol 12 (3) ◽

pp. 302

Author(s):

Rajesh Kumar ◽

Piyush Bhardwaj ◽

Gabriele Pfister ◽

Carl Drews ◽

Shawn Honomichl ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Information Dissemination ◽

Fine Particulate Matter ◽

Model Errors ◽

Air Quality Forecast ◽

Fine Particulate ◽

Regional Air Quality ◽

Forecasting System ◽

Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

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