scholarly journals Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Hao Luo ◽  
Yong Han ◽  
Xinghong Cheng ◽  
Chunsong Lu ◽  
Yonghua Wu
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Wind Speed ◽  
Wind Direction ◽  
Industrial Development ◽  
Northwest China ◽  
Temporal And Spatial Distribution ◽  
Ambient Exposure ◽  
Pm2.5 Concentration

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM10 (PM diameter < 10 μm ) and PM2.5 (PM diameter < 2.5 μm) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM10 caused by blowing dust in spring in NW and the intensive emissions of PM2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM2.5/PM10 ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM2.5 loadings.

scholarly journals Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Caroline Kiai ◽  
Christopher Kanali ◽  
Joseph Sang ◽  
Michael Gatari
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Airborne Particulate Matter ◽  
Spatial Extent ◽  
Airborne Particulate ◽  
Concentration Levels ◽  
Pm2.5 Concentration

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.

scholarly journals An approach for the identification of particulate matter in the clouds of Bogotá using satellite imagery analysis [Online First]

2019 ◽  
Vol 23 (2) ◽  
Author(s):  
Juan Antonio Aragón Moreno ◽  
Laura Isabel Espinosa Martínez ◽  
Paula Andrea Castañeda Garzón
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Wind Speed ◽  
Wind Direction ◽  
Monitoring Network ◽  
Cloud Formation ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Patterns Of Behavior ◽  
Model Correlation

Objective: An analysis of the air quality of Bogotá by identifying clouds during the period from 2013-2017 and verifying patterns of behavior between cloud formation and the concentration of particulate matter is presented. Materials and methods: The study sample includes data provided by the Bogotá Air Quality Monitoring Network (RMCAB), taking into account the concentration of particulate matter, temperature, precipitation, wind direction and wind speed. The data are compared with Landsat 8 satellite images and different combinations of spectral bands through the use of the Geographic Information System (GIS) ArcGis. Results and discussion: A high model correlation is reflected in a percentage greater than 90%, presenting a greater coincidence with a periodicity of two years during the dry period; it is possible to observe that the concentration of pollutants follows the trend of the wind vector lines, and the concentration has a direct correlation with cloud formation, which is influenced by temperature, wind speed and wind direction. Conclusions: This paper provides an alternative for the measurement of particulate matter and contributes to the collection of information on this research topic.

scholarly journals Application of hysplit model to evaluate the relationship between meteorological parameters and PM10 content in the atmospheric environment in Ha Noi city, Vietnam

2020 ◽  
Vol 3 (2) ◽  
pp. First
Author(s):  
Anh Dung Nguyen ◽  
Hồng Sơn Dương ◽  
Đức Hạnh Nguyễn Thế ◽  
Nguyen Dac Dong
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Wind Direction ◽  
Meteorological Parameters ◽  
Meteorological Factors ◽  
Pollution Sources ◽  
Atmospheric Environment ◽  
Hysplit Model ◽  
The Relationship

Meteorology is one of the factors that plays an important role in assessing the quality of the atmospheric environment. Regarding the air pollution, especially dust and gaseous emissions, there are currently few studies on the relationship between meteorological factors and the increase in pollutant concentration. In this study, the relationship between several meteorological parameters such as temperature (TEM), wind speed (WS), wind direction (WD) and PM10 content in Hanoi were evaluated through the Spearman correlation coefficient (r) by SPSS statistical analysis software. Data includes hourly meteorological factors (temperature, wind speed and wind direction) and 24-h PM10 concentration collected at three automatic air quality monitoring stations in Hanoi in 2018. In addition, HYSPLIT model is used to determine the influence of wind direction and contribution of air pollution sources. The results show a negative correlation (r <0) between PM10 content, temperature and wind speed in dry and rainy seasons. During the dry season, Hanoi has a higher PM10 content than the remaining months of the year. This might be partly affected by outside pollution sources from the North and Northwest. The findings emphasize the dependence of air quality on local meteorological conditions and the distribution of major

scholarly journals Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 788
Author(s):  
Rong Feng ◽  
Hongmei Xu ◽  
Zexuan Wang ◽  
Yunxuan Gu ◽  
Zhe Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Rural Areas ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Northwest China ◽  
Air Pollutant ◽  
Capital City ◽  
Air Pollution Control ◽  
Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

scholarly journals Effectiveness of SOx, NOx, and Primary Particulate Matter Control Strategies in the Improvement of Ambient PM Concentration in Taiwan

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 460
Author(s):  
Jiun-Horng Tsai ◽  
Ming-Ye Lee ◽  
Hung-Lung Chiang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Model Simulation ◽  
Control Strategies ◽  
Ambient Air ◽  
Clean Air Act ◽  
Control Measures ◽  
Control Plan ◽  
Clean Air ◽  
Pm2.5 Concentration

The Community Multiscale Air Quality (CMAQ) measurement was employed for evaluating the effectiveness of fine particulate matter control strategies in Taiwan. There are three scenarios as follows: (I) the 2014 baseline year emission, (II) 2020 emissions reduced via the Clean Air Act (CAA), and (III) other emissions reduced stringently via the Clean Air Act. Based on the Taiwan Emission Data System (TEDs) 8.1, established in 2014, the emission of particulate matter 2.5 (PM2.5) was 73.5 thousand tons y−1, that of SOx was 121.3 thousand tons y−1, and that of NOx was 404.4 thousand tons y−1 in Taiwan. The CMAQ model simulation indicated that the PM2.5 concentration was 21.9 μg m−3. This could be underestimated by 24% in comparison with data from the ambient air quality monitoring stations of the Taiwan Environmental Protection Administration (TEPA). The results of the simulation of the PM2.5 concentration showed high PM2.5 concentrations in central and southwestern Taiwan, especially in Taichung and Kaohsiung. Compared to scenario I, the average annual concentrations of PM2.5 for scenario II and scenario III showed reductions of 20.1% and 28.8%, respectively. From the results derived from the simulation, it can be seen that control of NOx emissions may improve daily airborne PM2.5 concentrations in Taiwan significantly and control of directly emitted PM2.5 emissions may improve airborne PM2.5 concentrations each month. Nevertheless, the results reveal that the preliminary control plan could not achievethe air quality standard. Therefore, the efficacy and effectiveness of the control measures must be considered to better reduce emissions in the future.

scholarly journals Influence of Urban-Growth Pattern on Air Quality in China: A Study of 338 Cities

2018 ◽  
Vol 15 (9) ◽  
pp. 1805 ◽  
Author(s):  
Yanchuan Mou ◽  
Yan Song ◽  
Qing Xu ◽  
Qingsong He ◽  
Ang Hu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Growth ◽  
Growth Pattern ◽  
Urban Form ◽  
Northwest China ◽  
City Size ◽  
Socioeconomic Indicators ◽  
Economic Zones ◽  
Coastal China

Air pollution in China is a serious problem and an inevitable threat to human health. This study evaluated the relationship between air quality and urban growth pattern in China by conducting empirical research involving 338 prefecture-level and above cities. Spatial regression techniques considering spatial autocorrelation were applied to correct the calculation bias. To obtain local and accurate results, a conception of eight economic zones was adopted to delineate cities into different groups and to estimate regression separately. An additional six urban form and socioeconomic indicators served as controlling variables. Significant and positive relationships between the aggregated urban growth pattern index and air pollution were observed in Northeast China, northern coastal China, and Northwest China, indicating that a high degree of urban aggregation is associated with poor air quality. However, a negative parameter was obtained in southern coastal China, showing an opposite association on urban aggregation and air quality. Nonsignificant connections among the other four zones were found. The findings also highlighted that land use mix, population density, and city size exerted varied and significant influence on air quality across eight economic zones. Overall, this study indicated that understanding the quantitative relationships between urban forms and air quality can provide policymakers with alternative ways to improve air quality in rapidly developing China.

scholarly journals Particulate matter air pollution offsets ozone damage to global crop production

2017 ◽  
Author(s):  
Luke D. Schiferl ◽  
Colette L. Heald
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Crop Production ◽  
Surface Ozone ◽  
Diffuse Radiation ◽  
Environmental Pressures ◽  
Global Food Security ◽  
Negative Effect ◽  
Pollution Exposure

Abstract. Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production is positive (+6.0 %, +0.5 %, and +4.9 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that more detailed physiological study of this response for common cultivars is crucial.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 &micro;g/m3 (IQR: 45.4-73.0 &micro;g/m3; median= 57.5 &micro;g/m3). For the ML LUR models, RMSE values ranged between 5.43 &micro;g/m3 - 15.43 &micro;g/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 &micro;g/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

scholarly journals Visualization of Correlation for Air Pollution Time Series using TAQMN Data

2019 ◽  
Vol 8 (3) ◽  
pp. 7922-7927
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Pearson Correlation ◽  
Fine Particulate Matter ◽  
Correlation Method ◽  
Well Being ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

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