Distribution Of PM10 Concentration Research Articles

Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China

Inhalable particulate matter (PM10) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM10, which poses a threat to human health. Therefore, exploring the relationship between PM10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM10 concentrations across China. The coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m3, and 8.826 μg/m3, respectively. Based on this, we analyzed the spatio-temporal distribution of PM10 concentrations in China from 2015 to 2021. High PM10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM10 concentration change to land-cover change. The PM10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

Observational study of river-land breeze and its influence on distribution of PM10 concentrations over a main mining city in the Yangtze River Delta, China

Wind data from air pollutant observation networks and meteorological stations are used to analyze the characteristics of river-land breeze near Tongling city (a main mining city in the Yangtze River Delta). The inhomogeneous distribution of pollutant matters near Tongling city due to river-land breeze is also investigated. Our results show the following conclusions. 1) The river breeze during the daytime is stronger than the land breeze at night. And the speed of river-land breeze is increased rapidly from 7:00 and arrived at the maximum at 12:00. After 15:00, the speed is slowed rapidly. 2) The river-land breeze in city area (east of Yangtze River, speed is .07 m/s) is weaker than the natural area (west of Yangtze River, speed is 0.18 m/s). Furthermore, the seasonal variations of breeze both in the west and east sides are different. In west side, the breeze is strongest in spring. And in the east side, the breeze is strongest in summer. 3) Under a weak breeze (≤0.45 m/s), the PM10 is moved by the breeze within the region and causes the heterogeneity. While with a strong breeze (>0.45 m/s), the PM10 is transported out of the region, and the PM10 concentration becomes homogenous. 4) The river breeze leads to a reduction of the pollutant concentration near the Yangtze River, but an increase in the city due to the transportation of pollutant particles from coast to city at daytime.

Study on the Ventilation Method to Maintain the PM10 Concentration in a Subway Cabin below 35 μg/m3.

The city of Seoul will limit the maximum particulate matter (PM10) concentration to ≤35 μg/m3 (from 2024). Herein, a numerical parametric study was conducted on the PM removal efficiency of the heating, ventilation, and air conditioning (HVAC) filters installed in the ceiling of subway cabins. The PM10 concentration distribution was explored according to the flow rate and flow rate ratio of the air introduced into the cabin. Under the current ventilation conditions of the subway train HVAC system, the PM10 concentration was highest in the cabin central area where exhaust outlets are located and decreased toward both ends of the cabin. The indoor airflow was improved and the PM10 concentration was reduced by increasing the flow rate of the supplied air at both ends of the cabin while decreasing it in the central area. It was found that the strengthened PM10 concentration criterion of Seoul can be met by increasing the ventilation flow rate to 700 CMH (currently, 500 CMH) and the filter efficiency to 85% (currently, 70%) while maintaining the current flow rate ratio. These results are expected to be used as important reference data for reducing the PM concentration in subway cabins and thereby improving indoor air quality.

Dry deposition of dust particles during medium- and high-level dust storms to a forest canopy in a semi-arid region

In the present study the impact of forest canopy on the spatial distribution of PM10 concentration during dust events in a region, which is subjected to frequent occurrences such as dust storms is investigated. The developed two-dimensional model is based on the application of theory of turbulent diffusion in the forest canopy in conjunction with model of dust particles deposition to vegetation elements. The leaf area density (LAD) distribution with height as well as dust particle size distribution are taken into account. The initial wind velocity profiles on the windward side of the forest were fitted from data obtained in field measurements in the studied region. The resulted dust transport in the forest canopy was validated by comparison of theoretical results with the empirical data in the studied region. Using the suggested model, we analyzed the evolution of wind velocity profiles in and above the forest canopy as well as the process of dry deposition of PM to vegetation elements. Numerical calculations performed using the developed model showed that in dust storms, PM10 concentrations at the leeward side of the forest are reduced by ≈ 16–18% depending on dust storm level. We also found that inside the canopy the ratio of PM10 concentrations between the points along the studied transect and windward side (not affected by the forest) is higher, the higher the level of the dust storm. Our model results of the atmospheric PM distribution show fairly good agreement with the data of the field measurements.

Urban aerosol assessment and forecast: Coimbra case study

The objective of this study is to explore the existing services at a regional scale and study their applicability to the assessment and forecast of air quality at the urban scale. Two approaches were evaluated to characterize urban background pollution levels from the regional operational forecast: (i) Copernicus Atmosphere Monitoring Service (CAMS) and (ii) Chemical Transport Model CHIMERE from the national forecast system. The local contribution from road traffic was analysed using transportation (VISUM) -emission (QTraffic) - dispersion (ADMS-Roads) modelling chain with 10 m grid resolution. The methodology was applied to Coimbra city to estimate PM10 concentrations for the period of one year (2018). In order to validate the model outputs against the daily observations, the FAIRMODE Delta Tool was applied separately for regional and urban scale. The results obtained for Coimbra satisfy the modelling performance criteria. However, both approaches used to characterize background concentrations underestimate PM10 levels. The modelling bias at urban traffic station is about 6 μg m−3 for annual mean concentration. Local highest contribution of road traffic to PM10 annual mean obtained with high grid resolution (10 m) achieves 22 μg m−3. Spatial distribution of PM10 concentrations highlights the limitations to characterize urban hot spots based on the measurements from only one traffic station available within the study area and used to validate the urban scale model.

Study on distribution of PM10 concentration and airflow using computational fluid dynamics at underground subway station

Study on distribution of PM10 concentration and airflow using computational fluid dynamics at underground subway station

Seasonal Particulate Matter (PM10) Concentration in Klang Valley, Malaysia

This paper presents a study on Seasonal Particulate Matter (PM10) concentration at selected region in Klang Valley for two years database (2002-2004). The study aimed to analyse on how the distribution of PM10 concentration has changed over monsoonal. Kriging method was employed for interpolating PM10 concentration over limited dataset from seven monitoring stations in Klang Valley. Results found that higher PM10 concentration was recorded during Southwest monsoon (65.92μg/m-3) followed by both inter-monsoon (62.17μg/m-3) and (59.13μg/m-3) respectively. Meanwhile, the lowest concentration (36.67μg/m-3) was recorded during northeast monsoon. From the distribution, Klang station has recorded the highest PM10 concentration throughout the season, followed by Kuala Selangor (59.33μg/m-3). Hulu Langat and Gombak stations have shown the lowest concentration of PM10, (40.92μg/m-3) and (36.67μg/m-3) respectively. The spatial distribution map indicated that the PM10concentration level is not only determining by seasonal but also depends on location of monitoring station itself. The concentration may increase if the station is closer to the proximity of urban and industrial pollution sources.

A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios

Exploring the spatial distribution of air pollutants under future urban planning scenarios is essential as urban sprawl increases in China. However, existing published prediction models usually forecast pollutant concentrations at the station level or estimate spatial distribution of pollutant in a historical perspective. This study has developed a hybrid Grey-Markov/land use regression (LUR) model (GMLUR) for PM10 concentration prediction under future urban scenarios by employing the forecast of Grey-Markov model as surrogate measurements to calibrate the spatial estimations of LUR model. Taking the agglomeration of Changsha-Zhuzhou-Xiangtan (CZT) in China as a case, the superiority of GMLUR was tested and spatial distribution of PM10 concentrations based on four potential land use scenarios for the year 2020 were predicted. Results show that GMLUR modelling outperforms LUR modelling with clear lower average relative percentage error (5.13% vs. 24.09%) and root-mean-square error (5.50 μg/m3 vs. 21.31 μg/m3). The economic interest scenario identifies the largest demands of future built-up (2 306.50 km2) and bare (34.88 km2) areas. Built-up area demands for the business as usual scenario, resource-conserving scenario, and ecological interest scenario are 362.67, 1 042.22, and 1 014.70 km2, respectively. Correspondingly, the economic interest scenario identifies the severest PM10 pollution with the highest mean predicted concentration of 53.78 μg/m3 and the largest percent (19.43%) of area exceeding the Level 2 value (70 μg/m3) of Chinese National Ambient Air Quality Standard (CNAAQS); these are significantly higher than those of the business as usual scenario (49.63 μg/m3, 6.28%). The resource-conserving scenario (46.79 μg/m3) and ecological interest scenario (46.76 μg/m3) are cleaner with no area exceeding the Level 2 value of CNAAQS. It can be concluded that GMLUR modelling provides a feasible way to evaluate the potential outcome of future urban planning strategies in the perspective of air pollution.

Spatial analysis between particulate matter and emergency room visits for conjunctivitis and keratitis

BackgroundThe concentration of particulate matter in the air varies depending on the region because it is lightweight and generated from a variety of sources. To assess the relationship between particulate matter and eye disease, this study analyzes the concentration data obtained from spatial analysis of particulate matter and emergency visit data.MethodsThe study included 769 residents of Daegu, Korea who had visited an emergency room for the problem of conjunctivitis or keratitis. Concentrations of PM10 and other air pollutants were obtained from the Korean Ministry of the Environment. PM10 concentrations and the number of patients from each of 143 administrative dongs (sub-municipal level administrative units) of the city of Daegu were obtained using spatial analysis. The patient distribution and PM10 concentration were mapped for comparison, and their relationship was examined using scatter plot, regression analysis, and the independent sample t-test.ResultsThe number of patients with conjunctivitis and keratitis was significantly higher in the regions of the top 20% areas than the bottom 20% areas in terms of PM10 concentration. The distribution of PM10 concentration and number of patients was visually similar on the map. The concentration of PM10 and the number of patients showed a dose–response relationship. When the concentrations of other air polluta9nts were controlled for, the numbers of conjunctivitis and keratitis patients were 0.04 per 1000 ER patients and 0.10 per 1000 ER patients, respectively.ConclusionAs PM10 is associated with the prevalence of conjunctivitis and keratitis, measures to reduce particulate matter through environmental methods are needed.

Characterising the Seasonal Variations and Spatial Distribution of Ambient PM10 in Urban Ankara, Turkey

The urban airshed of Turkey is under the influence of both traffic and heating emissions. Periodic monitoring and management of air pollution is a must for an up-to-date exposure assessment for effective health and environmental management. The main objective of this paper is to assess the spatial and temporal variability of particulate matter (PM10) in urban areas of Turkey using the metropolitan area of Ankara as a case study, exploring the seasonal variation of PM10 concentrations resulting from indoor heating and transport. This study employed spatiotemporal kriging with external drift to examine the space-time variability and distribution of PM10 concentrations in Ankara for year 2015. The measurements of PM10 in and around metropolitan Ankara were carried out at nine stations. Measurements for the period December 2014 – November 2015 were used and averaged using a 6-h interval for each season: Winter (Dec, Jan, Feb), Spring (Mar, Apr, May), Summer (Jun, Jul, Aug) and Autumn (Sep, Oct, Nov). Population distribution, used as a proxy for indoor heating, and proximity to roads were used as auxiliary variables for kriging analysis. PM10 concentration levels were observed to be highest during autumn and winter. Summer was identified as the “cleanest” period although, in some regions of the city, pollution levels remained high. The southeast region of metropolitan Ankara was identified to be the most polluted region of the city after the city centre.

Very large scale motions and PM10 concentration in a high-Re boundary layer

In this letter, the concentration of particles with size less than 10 μm (PM10) and flow velocities are measured synchronously at different heights in a sand storm, a high-Reynolds number multi-phase turbulent boundary layer. It is revealed that the low-speed very large scale motions (VLSMs) are recurrent with a frequency dominating the streamwise velocity power spectra and modulate the streamwise distribution of PM10 concentration. In the logarithmic layer, the low-speed VLSMs correspond to weak shear stress on the bottom surface and hence decrease the PM10 upward flux. In the higher region, the high-speed VLSMs enhance both the streamwise and the upward transportation of PM10 because of the positive correlation between the velocity and the particle concentration perturbations.

Research on the optimization of air quality monitoring station layout based on spatial grid statistical analysis method

ABSTRACTIn recent years, with the significant increase in urban development, it has become necessary to optimize the current air monitoring stations to reflect the quality of air in the environment. Highlighting the spatial representation of some air monitoring stations using Beijing’s regional air monitoring station data from 2012 to 2014, the monthly mean particulate matter concentration (PM10) in the region was calculated and through the IDW interpolation method and spatial grid statistical method using GIS, the spatial distribution of PM10 concentration in the whole region was deduced. The spatial distribution variation of districts in Beijing using the gridding model was performed, and through the 3-year spatial analysis, PM10 concentration data including the variation and spatial overlay (1.5 km × 1.5 km cell resolution grid), the spatial distribution result obtained showed that the total PM10 concentration frequency variation exceeded the standard. It is very important to optimize the layout of the existing air monitoring stations by combining the concentration distribution of air pollutants with the spatial region using GIS.

Modeling PM 10 in Ho Chi Minh City, Vietnam and evaluation of its impact on human health

Abstract According to World Health Organization (WHO) and Global Burden of Disease, ambient air pollution is estimated to be responsible for 3.7 million premature deaths in 2012 [1] . Therefore, it is urgent to estimate the impact of air pollution on public health and economic damage. The objectives of this research are: study the distribution of PM10 concentration over Ho Chi Minh city (HCMC) and relationship to public health and for proposing solutions of disease s prevention in HCM, Vietnam. EMIssion SENSitivity model was applied to conduct air emission inventory for transportation sector. Then, Finite Volume Model and Transport and Photochemistry Mesoscale Model were used to simulate the meteorology and the spatial distribution of PM10 in HCMC. Together with disease data obtained, the US Environmental Benefits Mapping and Analysis Model was applied for calculating the number of deaths and estimating economic losses due to PM10 pollution. Finally, solutions to reduce PM10 pollution and protect public health are proposed. The results showed that the highest 1-h average concentration of PM10 is 240 μg m−3 in North Eastern of HCMC. The concentration of PM10 for annual average in District 5 ranged from 17 to 49 μg m−3. There are 12 wards of District 5 with PM10 concentration exceeding the WHO guidelines (20 μg m−3 for annual average of PM10 and 50 μg m−3 for 24-h average). The high concentration of PM10 causes 5 deaths yr−1 in District 5 and 204 deaths yr−1 in HCMC, and it causes economic losses of 1.84 billion of USD.

Characterization of Background Aerosol Properties during a Wintertime Smog Episode

The aim of this paper is to study the wintertime physical properties of atmospheric aerosol particles on the basis of data observed at the K-puszta regional background station in Hungary. In Hungary wintertime smog episodes are linked to strong stable air (high pressure blocking events) with thermal inversion. These atmospheric conditions are frequently formed during winter months (November–February) due to the special geographical location of the country. The formation of smog events is highly probable in cases of thermal inversion periods sustaining for at least 4 days. We discuss in the paper the role of high-pressure blocking events in aerosol properties in terms of PM10 concentrations, aerosol size distributions, new particle formation and optical properties. We found that high-pressure blocking events have significant impacts on the size distribution and particle formation processes. At K-puszta the aerosol is in highly aged state with size distribution dominated by the accumulation mode. This is further supported by the optical properties, e.g., by high scattering Angstrom exponent and by relatively weak absorption. The most significant effect of extreme episodes is manifested in the changes in PM10 concentrations and, consequently, in aerosol optical properties. The PM10 concentrations, scattering coefficients and absorption coefficients considerably increase to extreme values that are characteristic of a heavily polluted atmosphere rather than rural air. Our results indicate that in winter, the air quality at Kpuszta is often influenced by regional air pollution as shown by spatial distribution of PM10 concentration. It is found that PM10 had almost the same concentration in regional background air and in different types of urban environments. The special meteorological conditions and the role of regional-scale transport can explain why local abatements in cities cannot lead to significant improvement of the air quality during smog events.

Emission Scenarios and Health Impacts of Air Pollutants in Goa

ABSTRACTThis paper presents the first emission inventory for the highly urbanized state of Goa in India, which is an important international tourist destination and also accommodates significant industrial activities, including mining. The observed concentrations of the pollutants like PM10 and PM2.5 show violations at many locations. Sectoral inventories prepared in this study depicts mining (38%), industries (24%), and transport (10% tail-pipe and 15% road dust) as the major contributors to the PM10 emissions in the state. Higher emissions intensity is observed in heavily populated and industrialized coastal taluks and mining dominated taluks. Emissions are projected for the future (2030) under two different scenarios (business as usual and alternative) to assess future air quality and impacts. The grid-wise emissions under these future scenarios are fed into an air quality model, to estimate spatial distribution of PM10 concentrations in Goa. The model results are validated with actual observations. Thereafter, the grid-wise PM10 concentrations are overlaid on the population to compute its health impacts using established dose response functions. The study shows that PM10 accounts for 2.6% of mortalities in Goa, which are expected to go up further in a business as usual scenario. Alternative strategies which show reduction in pollution and associated health impacts in the region are evaluated. Based on the alternative scenario, key recommendations are made for air quality improvement in the state.

Assessment of human health impact from PM 10 exposure in China based on satellite observations

Assessment of human health impact from the exposure to PM10 air pollution is crucial for evaluating environmental damage. We established an empirical model to estimate ground PM10 mass concentration from satellite-derived aerosol optical depth and adopted the dose-response model to evaluate the annual average human health risks and losses related to PM10 exposure over China from 2010 to 2014. Unlike the traditional human health assessment methods, which relied on the in situPM10 concentration measurements and statistical population data issued by administrative district, the approach proposed in this study obtained the spatial distribution of human health risks in China by analyzing the distribution of PM10 concentration estimated from satellite observations and population distribution based on the relationship to the spatial distribution of land-use type. It was found that the long-term satellite observations have advantages over the ground-based observations in estimating human health impact from PM10 exposure.

Spatial analysis of PM<sub>10</sub> and cardiovascular mortality in the Seoul metropolitan area

ObjectivesNumerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than 10 μm in aerodynamic diameter (PM10). The aim of the present study was to examine the spatial distribution of PM10 concentrations and cardiovascular mortality and to investigate the spatial correlation between PM10 and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model.MethodsFrom 2008 to 2010, the spatial distribution of PM10 in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between PM10 concentrations and cardiovascular mortality.ResultsAn examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggido than in other regions. In a comparison of PM10 concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that PM10 has a statistically significant relation to cardiovascular mortality.ConclusionsTo investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of PM10.

Temporal and spatial PM10 concentration distribution using an inverse distance weighted method in Klang Valley, Malaysia

PM10 is one of the air contaminants that can be harmful to human health. Meteorological factors and changes of monsoon season may affect the distribution of these particles. The objective of this study is to determine the temporal and spatial particulate matter (PM10) concentration distribution in Klang Valley, Malaysia by using the Inverse Distance Weighted (IDW) method at different monsoon season and meteorological conditions. PM10 and meteorological data were obtained from the Malaysian Department of Environment (DOE). Particles distribution data were added to the geographic database on a seasonal basis. Temporal and spatial patterns of PM10 concentration distribution were determined by using ArcGIS 9.3. The higher PM10 concentrations are observed during Southwest monsoon season. The values are lower during the Northeast monsoon season. Different monsoon seasons show different meteorological conditions that effect PM10 distribution.

Application of Statistical Distribution of PM10 Concentration in Air Quality Management in 5 Representative Cities of China

ObjectiveTo estimate the frequency of daily average PM10 concentrations exceeding the air quality standard (AQS) and the reduction of particulate matter emission to meet the AQS from the statistical properties (probability density functions) of air pollutant concentration. MethodsThe daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi'an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the lognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method. ResultsThe daily PM10 average concentration in the 5 cities was fitted using the lognormal distribution. The exceeding duration was predicted, and the estimated PM10 emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AQS. ConclusionAir pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.

STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.