Distribution Of Air Pollutants Research Articles

Changes in the Distribution of Air Pollutants (Carbon Monoxide) during the Control of the COVID-19 Pandemic in Jakarta, Surabaya, and Yogyakarta, Indonesia

Changes in the Distribution of Air Pollutants (Carbon Monoxide) during the Control of the COVID-19 Pandemic in Jakarta, Surabaya, and Yogyakarta, Indonesia

Coupling Relationship Between Heat Waves and Air Pollution: A Case Study of Central Tianjin

With the increasing contradiction between urban construction and ecological and societal resources, the impact of heat wave and air pollution on cities has become increasingly conspicuous. How to create a comfortable and cool urban environment has become a hot topic that the whole society attaches great importance to. By analyzing the spatio-temporal distribution of heat wave and air pollution, the coupling characteristics of heat wave and air pollution are obtained.

Air Pollution Prediction Based on Changes in Monsoon Wind Direction by Using Trajectory-Geospatial Approach

Industrial areas are typically associated with hazardous levels of air pollution to human health and the environment. The growing number of factories in the area poses an ever-greater threat to the surrounding communities. One of the several incidents pointing to poor air quality in the industrial region is the severe air pollution incident that occurred in Pasir Gudang in June 2019 which brought adverse health impacts to nearby schoolchildren. This study intended to ascertain the role of meteorological factors and impacts on the dispersion of air pollution in the Pasir Gudang Industrial Area at the time of the occurrence. The air pollution distribution patterns were predicted using a trajectory-geospatial method, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Geographic Information System to account for the impact of the monsoon by determining which areas might be hit the worst as the shifted of monsoon seasons. Based on the results, the forward-air mass trajectories showed direct influence by the wind changes in monsoon seasons (Southwest Monsoon, Northeast Monsoon, and Inter-Monsoon). The geospatial maps showed the potential areas affected by the air pollution incident were highly distributed within 1 km, associated with lower dispersion of air pollution at 1000m above ground level height. The findings can serve as a guideline for local authorities in decision-making to develop better standard operating procedure in managing future air pollution threats and to improve industrial location planning in reducing air pollution impacts on the surrounding area.

Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring

Air pollution is a major threat to public health. However, two issues have not been adequately addressed in most conventional Land Use Regression models for air pollution prediction: 1). A combination of urban forest involvement and urban form representation; 2). Scale sensitivity analysis of model variables. Here, we apply lacunarity to investigate the spatial sensitivity of predictors, incorporate 2-D and 3-D urban form to comprehensively characterize the urban environment, and examine the tree diversity impacts on air pollution distribution using unique NO2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California. We find that lacunarity-optimized models could reduce the computation burden by extracting the upper limits of the spatial heterogeneity of predictors while keeping the model accuracy simultaneously. Furthermore, there are synthetic effects between the urban form and tree diversity on NO2 distribution, and such effect directions could be non-monotonic. Finally, although the increase in tree diversity could facilitate the reduction of regional NO2 concentration, it is essential to seek a balance between tree diversity and tree dominance to effectively improve air quality on the city scale. The findings are useful for environmental scientists striving for better air quality and urban planners caring for the well-being of cities.

Using urban form to increase the capacity of cities to manage noise and air quality

The top two environmental factors adversely affecting human health in Europe are air and noise pollution, with road traffic being the largest source. Urban density plays an important role in reducing car traffic. However, the benefits of reduced emissions per capita can still mean higher emissions locally, because of the number of people in the area. Therefore, this paper investigates how morphological parameters influence the local distribution of noise and air pollution. A parametric approach, based on the Spacematrix method, is used to study the impact of morphological parameters on the distribution of air and noise pollution, controlling for traffic mode, flows and speed. To compare the impact of exposure to noise and air pollution, their respective health burden is calculated using disability- adjusted life years (DALYs). The results, based on 31 models of different forms, show that the degree of openness greatly affects performance with opposite effects for noise and air pollution. Building types with slightly open yards, like open corner blocks, may provide an attractive compromise solution due to their relatively good noise exposure situation at the same time as the dispersion of air pollutants improves. Adding sound absorbing vegetation is an effective measure to mitigate noise, especially for blocks with openings, limiting the propagation of sound into the yard. Further, densification is beneficial for health if the increase in density does not increase traffic volume in the same proportion. Densification by adding towers on a perimeter building block gives the best results for health as it combines a less noisy yard, thanks to the enclosure of the yard with towers, which enhances turbulent mixing of air within the street canyon.

Monitoring of Spatio-Temporal Dynamics of Air Pollution in Enugu Urban using Remote Sensing and GIS

Ambient air pollution is widely acknowledged to be the worst type of environmental pollution. The amount of air pollution varies from time to time and from region to region. The spatiotemporal distribution of air pollutants in the city of Enugu was investigated in this study. Due to the high concentration of industrial clusters, heavy traffic and increasing population density in the study area, notable places were purposely sampled during the hours of 8a.m. and 4p.m. A portable gas analyzer, the Aerocet-531 Met One Instrument, Drager X-am 5000, was used to quantify the amounts of PM2.5, PM10, CO, SO2, and NO2. The GPS coordinates of the sampling spots were recorded using the portable Germin-300 Global Positioning System (GPS) device analyzer, which aided in the data processing to create spatial interpolation maps in ArcMap. GIS and remote sensing methods were utilized to analyze the spatial distribution of contaminants using satellite images. ArcGIS and Erdas Imagine were used to analyze the identification of pollution sources, the geographical distribution of pollutants in connection to land use and the epidemiological scope of pollutants from sources. The approaches employed to get the findings included image categorization, proximity, intersection, and interpolation. It was determined that there was a correlation between the landuse density and the levels of NO2, SO2, CO, PM2.5, and PM10. Within 100m – 400m of major roadways, correlation values were highest, indicating a high risk of vulnerability in this area. The maximum values for PM2.5 and PM10 were 1.78μg/m3 and 205μg/m3 respectively during the dry season and 2.13μg/m3 and 184μg/m3 during the wet weather. Similarly, the minimum values for PM2.5 and PM10 are 0.25μg/m3 and 161μg/m3 respectively during the dry season and 0.45μg/m3 and 132μg/m3 during the wet weather. The study further shows that the range values of SO2, NO2, and CO during the dry season are between 0.71 to 1.98ppb, 0.72 to 1.96ppb and 9.21 to 18.04ppb respectively while ranges of 0.45 to 1.73ppb, 0.31 to 0.89ppb, and 7.94 to 12.56ppb were recorded during the wet season. The study concluded that the high levels of PM2.5, PM10, SO2, NO2, and CO in the study area monitored especially in the dry season may present a potential public health risk. Sequel to this, it is advised that the study area should reduce their exposure in light of this, especially during the dry season.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources.

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO2, NO2, CO, O3, PM2.5, and PM10 were 8.61-13.76μgm-3, 26.53-36.06μgm-3, 0.79-1.31mgm-3, 82.24-87.62μgm-3, 37.98-51.10μgm-3, and 84.15-97.47μgm-3. The concentrations of air pollutants (except O3) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM10 in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Spatial Distribution of PM10 and NO2 in Ambient Air Quality in Cape Town CBD, South Africa

Fixed air quality monitoring stations generally monitor the air quality in developing countries. However, this practice, in addition to being costly, inherently contains drawbacks associated with the inability to capture the spatial distribution of air pollutants. Against this limitation, it is necessary to employ flexible and dynamic monitoring techniques that are fundamental and influential in comprehending the spatial distribution of pollutants. Because of this, in recent times, the application of GIS as a monitoring technique has proved to be more efficient than using fixed monitoring stations. Therefore, to this end, the current study mapped the spatial distribution of PM10 and NO2 pollutants in Cape Town CBD using the GIS technique. Subsequently, the GIS monitoring technique revealed that both pollutants had high spatial distribution between 2017 and 2018, irrespective of the season. Furthermore, high exposure concentrations of PM10 were generally observed across the CBD in contrast to NO2 exposure levels, which were relatively low. To contextualize the findings, compared with other studies, the current research discovered that spatial distribution of air pollution is associated with meteorological conditions, such as wind speed and temperature, that traditional techniques of monitoring exposure can’t capture.

Vertical characteristics and potential sources of aerosols over northeast China using ground-based MAX-DOAS

Information on the vertical distribution of air pollutants is essential to understand the potential mechanisms of haze pollution. Multiaxial differential optical absorption spectrometry (MAX-DOAS) is an effective method to detect the vertical distribution of air pollutants. In this study, we used MAX-DOAS to continuously analyze the vertical aerosol distribution throughout the year (from January 2019 to December 2019) in Shenyang. In winter, the aerosol extinction coefficient (AEC) was the highest (0.63 ± 0.09 km−1), followed by summer (0.53 ± 0.08 km−1), spring (0.51 ± 0.08 km−1), and autumn (0.47 ± 0.09 km−1). The aerosol extinction coefficients in various seasons were affected by the weather type. The hygroscopic growth of small particles lead to a surface AEC reaching 1.45 ± 0.23 km−1 in summer, resulting in heavy haze. The coal burning for heating and automobile exhaust in winter increased the possibility of winter haze, wherein the surface AEC can reach 1.27 ± 0.19 km−1. Signs of aerosol transport can be observed at high latitudes in summer, and when combined with backward trajectories, the transport of pollutants from Inner Mongolia to Liaoning Province is evident. The possible sources of air pollutants were investigated using a concentration weighted trajectory (CWT) model. In addition to the nearby Liaoning Province, both Beijing and Tianjin were also the main sources of pollution in Shenyang (0.04 km; Shandong Province was the main source of pollution at 0.44 km).

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

Industrial regions and relevant infrastructures are known to contribute to air pollutant emissions; thus, a detailed investigation of the air pollutant concentrations of a region based on specific land uses, with spatial reasoning, can support smart regional planning. However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited. Thus, in this study, we aimed to identify the factors that affect air-pollutant concentrations due to local spatial impacts in industrial regions across Australia. Considering the large spatial scale, the impact of a global factor can be overwhelmed by another factor due to local spatial impacts, and the phenomenon is a kind of spatial disparity. We developed a novel set of methods, including a point-of-interests-based spatial identification method and geographically weighted regression (with standardised coefficients), to: (i) identify the industrial regions in the study area, (ii) collect the remote sensing factors, and (iii) identify the factors that affect the spatial disparity of air-pollutant concentrations in industrial regions. The results indicated a significant spatial disparity in the air pollutant concentrations in the industrial region, at a continental scale. Anthropogenic factors significantly affected the spatial patterns of air pollutant concentrations in the industrial regions that were remote to cities, whereas meteorological and topographical factors had significant impacts on the air pollutant distributions in urban industrial regions. Furthermore, within the nationwide industrial lands, drives of the relatively high concentrations of ozone and sulphur dioxide, the drivers of the air pollutant concentrations were environmental factors; high concentrations of nitrogen dioxide were more associated with the topographical features of the region. The methods proposed in this study can serve as a reliable framework for analysing the air quality of industrial regions and can also, supplement future studies on emissions reduction in industrial parks.

The Effects of the Exhaust Fan Position to Indoor Air Pollution Distribution in Enclosed Parking Garage

Nowadays, lack of space in city center has forced the architects a propose a vertical construction to provide public facility, including the parking garage. Generally, this vertical parking garage has enclosed environment condition, which could be constructed under the building or in the middle of other buildings. This environment causes the parking garage will not have an adequate fresh air circulation or air change rate and gives a more risk due to the accumulation of air pollutants which is emitted from the cars inside. In this research, it observes a simple enclosed parking garage which normally can accommodate six cars. The air pollutant, particularly Carbon Monoxide (CO), from three gasoline cars is extracted by an exhaust fan in a proper location. Two location of exhaust fan are proposed to investigate better air circulation and better air pollutant extraction from the parking garage. The first option (Position of A) installs the exhaust fan near to the parking space units and the other installs the exhaust fan near to the pathway. Each option will follow two worst condition of simulations which are predicted will give a significant impact to Indoor Air Quality (IAQ). Each simulation has to extract the emission from three idle cars with same rate of air circulation of six air change per hour. The results show the influence of air flow pattern to the dispersion of air pollutant. An adequate air flow near to the emission source will cause the increase the maximum of air pollutant concentration. On the other hand, it can minimize the spread of air pollutant dispersion.

Prediction of the Tropospheric NO2 Column Concentration and Distribution Using the Time Sequence-Based versus Influencing Factor-Based Random Forest Regression Model

The prediction of air pollutants has always been an issue of great concern to the whole of society. In recent years, the prediction and simulation of air pollutants via machine learning have been widely used. In this study, we collected meteorological data and tropospheric NO2 column concentration data in Beijing, China, between 2012 and 2020, and compared the two methods of time sequence-based and influencing factor-based random forest regression in predicting the tropospheric NO2 column concentration. The results showed that prediction of the tropospheric NO2 column concentration using random forest regression was affected by the changes of human activities, especially emergency events and policy variations. The advantage of time sequence analysis lies in its ability to calculate the distribution of air pollutants with a long-time scale of prediction, but it may produce large errors in numerical value. The advantage of influencing factor prediction lies in its high precision and that it can identify the specific impact of each influencing factor on the NO2 column concentration, but it needs more data and work quantities before it can make a prediction about the future.

Contrasting Vertical Circulation between Severe and Light Air Pollution inside a Deep Basin: Results from the Collaborative Experiment of 3D Boundary-Layer Meteorology and Pollution at the Sichuan Basin (BLMP-SCB)

Abstract The study of air pollution in a valley is a classic research subject. Compared with flat terrain, the formation and development of haze pollution are more complicated and unique within a deep basin. How a basin or valley plays a role in the horizontal and vertical distribution of air pollutants is poorly understood in highly industrialized deep basins in China due to scarce field observations. We conducted a collaborative experiment of three-dimensional (3D) boundary layer meteorology and pollution at the western Sichuan Basin (SCB) close to the Tibetan Plateau (TP). Generally, the concentrations of PM1 (particulate matter smaller than 1.0 μm), NO, and NO2 largely decline with elevation, while O3 shows a slight increasing trend inside the SCB. Three different types of pollutant profiles and the formation mechanisms are described below. The high PM1 near the surface layer corresponds to the vertical clockwise circulation, i.e., a wind shift with increasing altitude in a clockwise direction. The air pollutants at the central and eastern SCB can be transported to the eastern foothills of the TP by southeasterly winds and then are trapped within the western SCB by a strong surface temperature inversion. The pollutants over the eastern TP also can be dispersed to above the SCB by westerly winds. More aerosol particles are concentrated at about 2.0 km MSL by jointly ascending and descending motion below and above the layer over the valley. The relative uniform PM1 in the vertical direction correlates to the counterclockwise circulation. The trapped pollutants in the western SCB can be transported to the eastern region by westerly winds and then are dispersed to the upper air by unstable stratification.

Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(O3 2018-03-01:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(O3 2018-03-01:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)

Spatial distribution of various air pollutants in China at 1 km(O3 2015-01-01:2018-02-28)