Variations Of PM10 Concentrations Research Articles

The role of large-scale atmospheric circulations on long-term variations of PM10 concentrations over Turkey.

PM10 is widely identified as an important atmospheric pollutant posing a serious threat to human health and environment as well as it influences the climate system. To unearth the mechanism involved in its sources and circulation behavior in environment, this study focuses on the role of large-scale atmospheric circulation on the long-term variability of PM10 over Turkey by applying rotated empirical orthogonal functions (REOF) analysis. As a result of the implementation of REOF to the daily PM10 data for 80 air quality stations throughout the period 2010-2020, first REOF mode (REOF1 44.9% in winter, 43.2% in spring, 39.5% in summer and 31.6% in fall) for all the four seasons indicated the role of local emission sources on the variations of PM10, which show high PM10 values in different geographical regions. The results of the second mode (REOF2, 17.9% in winter, 14.0% in spring, 14.0% in summer and 16.3% in fall) indicate the role of large-scale atmospheric circulations on the values of PM10. From the REOF2 analysis and extracted synoptic composite maps, the strength of southerly winds and the presence of southwesterly winds at low levels are very important in transporting of dust pollutants from the Arabian Peninsula and Northern Africa, respectively, to the eastern (EAR) and southeastern (SEAR) regions of Turkey during winter. In spring, sand particles in the interior terrestrial part of the country are carried to the northern regions by the effect of large-scale southerly winds, which cause above-normal PM10 concentrations in the Black Sea region of Turkey. In summer, dust particles together with warm dry air intrusion to the eastern region of Turkey by strong easterly winds are sourced by Caspian Sea and result in high PM10 values. Our findings emphasize that the long-term variations in air quality over Turkey are affected secondary by the variations in the large-scale atmospheric circulations with primary contributions from the changes in local emission sources.

Prediction of air quality using vertical atmospheric condition and developing hybrid models

Most metropolitan areas experience increasing health and environmental pressure from air pollutants. Air pollutant concentrations are mostly influenced by meteorological factors, pollutant sources, and topography. The vertical atmospheric conditions can affect the surface air pollution concentration. In this paper, to evaluate the relationship between the vertical meteorological characteristics and air pollution concentrations (CO, O3, PM2.5, PM10 and SO2) four different models including multi-linear regression (MLR), multi-linear perceptron (MLP), generalized regression neural network (GRNN), and adaptive Neuro-Fuzzy inference system (ANFIS) were developed. The vertical temperature difference, relative humidity, U and V wind components in different altitudes were extracted from the operational archive of the European Center for Medium Range Weather Forecast (ECMWF 2019) from March 2011 to December 2017. The variance inflation factor (VIF) and tolerance tests were utilized to remove the independent variables, which made multi-collinearity. Results showed that among the studied air pollutant variables, variation of PM10 concentration was more influenced by vertical meteorological characteristics obtained using the GRNN model with R2 = 0.960, root Mean square error (RMSE) = 0.019 and mean absolute error (MAE) = 0.005 in tearing phase and R2 = 0.940, RMSE = 0.054 and MAE = 0.019 in testing phase. The results of ANFIS model for PM2.5 concentration were acquired highest among the other air pollutant variables in tearing phase (R2 = 0.819, RMSE = 0.036 and MAE = 0.025) and in testing phase (R2 = 0.960, RMSE = 0.001 and MAE = 0.001). The results illustrated that GRNN and ANFIS models outperform other models for air pollutants prediction using vertical atmospheric conditions in the Sanandaj, Iran.

Analysis and Sources Identification of Atmospheric PM10 and Its Cation and Anion Contents in Makkah, Saudi Arabia

In this paper, atmospheric water-soluble cation and anion contents of PM10 are analysed in Makkah, Saudi Arabia. PM10 samples were collected at five sites for a whole year. PM10 concentrations (µg/m3) ranged from 82.11 to 739.61 at Aziziyah, 65.37 to 421.71 at Sanaiyah, 25.20 to 466.60 at Misfalah, 52.56 to 507.23 at Abdeyah, and 40.91 to 471.99 at Askan. Both daily and annual averaged PM10 concentrations exceeded WHO and Saudi Arabia national air quality limits. Daily averaged PM10 concentration exceeded the national air quality limits of 340 µg/m3, 32% of the time at Aziziyah, 8% of the time at Sanaiyah, and 6% of the time at the other three sites. On average, the cations and anions made a 37.81% contribution to the PM10 concentrations. SO42−, NO3−, Ca2+, Na+, and Cl− contributed 50.25%, 16.43%, 12.11%, 11.12%, and 8.70% to the total ion concentrations, respectively. The minor ions (F−, Br−, Mg2+, NO2−, and PO43−) contributed just over 1% to the ion mass. Four principal components explained 89% variations in PM10 concentrations. Four major emission sources were identified: (a) Road traffic, including emission from the exhaust, wear-and-tear, and the resuspension of dust particles (F−, SO42−, NO3−, Ca2+, Na+, Mg+, Br−, Cl−, NO2−, PO43−); (b) Mineral dust (Cl−, F−, Na+, Ca2+, Mg2+, PO43−); (c) Industries and construction–demolition work (F−, SO42−, Ca2+, Mg2+); and (d) Seaspray and marine aerosols (Cl−, Br−, Mg2+, Na+). Future work would include an analysis of the metal contents of PM10 and their spatiotemporal variability in Makkah.

Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model

BACKGROUND AND OBJECTIVES Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 – 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration. By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation.

Spatial and Temporal Variations in PM10 Concentrations between 2010-2017 in South Africa.

Particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10 µg/m3) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM10 concentration across the provinces. In addition, potential influencing factors of PM10 variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM10 concentration variation shows PM10 concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM10 air quality guideline of 15 µg/m3 in Gauteng province during the winter season. Temporally, the highest weekly PM10 concentrations of 51.4 µg/m3, 46.8 µg/m3, 29.1 µg/m3 and 25.1 µg/m3 at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM10 levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM10 levels was reported at most sites in Western Cape and KwaZulu-Natal.

Modeling for the source apportionments of PM10 during sand and dust storms over East Asia in 2020

Sand and dust storms occur frequently in East Asia, and the dust particles could be transported to most areas of East Asia. To quantify the dust emissions in 18 dust source regions in China and Mongolia and their contributions to PM10 concentrations over East Asia, 10 sand and dust storms (SDSs) episodes in 2020 were simulated using the Comprehensive Air Quality Model with Extensions (CAMx). A dust emission scheme and a tagged method were incorporated. The model well captured the variations of PM10 concentrations during the SDSs episodes. The Gobi deserts in Mongolia were the most important sources of sand and dust in East Asia, with the highest dust emissions, contributing more than 60% to the PM10 concentrations over East-Central China, Japan, and South Korea. The dust emission from Inner Mongolia was about 1/3 of that in Mongolia, and contributed 20–30% to PM10 concentrations over East-Central China, Japan, and South Korea. However, more than 50% of the PM10 concentrations in Inner Mongolia originated from Mongolia in the SDSs events. Few dust aerosols from Taklimakan and Eastern Xinjiang could be transported to the areas further than its surrounding area. Because of the larger contribution of Mongolia to Northern China, the variation of the dust storm frequency in Northern China correlated well with the surface conditions in Mongolia. Anthropogenic emissions contributed less than 5% to the PM10 concentrations over most part of East Asia during the SDSs events.

The study of variation of PM10 concentrations with meteorological conditions in ambient air

A review of the annual PM10 concentrations over 3 years showed the study area was subjected to severe particulate pollution. Meteorological data of Hyderabad city obtained from Central Pollution Control board (CPCB) at ICRSAT monitoring station. Investigations of daily PM10 concentration variation with the meteorological records were done it was identified that a unique aspect of the monsoon climate, changes in wind direction, and wind speed mainly governs the general trend of concentration of PM10 within each year. The results in this study are obtained by graphical representation of the collected data.

A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima

Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM10 concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM10 are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM10 was the HCH station. The highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μμg/m3. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and a statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio-temporal variations of PM10 concentrations. Furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM10 levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district.

Long-term Characterization of Urban PM10 in Hungary

Over urban areas in Hungary, the annual average PM10 concentrations are not frequently higher than 40 µg m–3. Despite the mitigation efforts of the local governments, the annual number of exceedances of the daily limit of 50 µg m–3 is higher than what is outlined in EU Directive No 2008/50/EC. The goal of the present study is to assess the characteristics of the temporal (annual, seasonal, daily) variations in PM10 concentrations in selected Hungarian cities with large populations, where most of the exceedances have been reported. The impacts of meteorological conditions on the measured PM10 concentrations and their temporal variations are also evaluated. An important aspect of studying the trends of air pollution is that the tendencies depend not only on the emissions of certain pollutants but also on the meteorological conditions in the area of interest. To analyse emission-related trends, the meteorological signal must be removed from the data series. In this study, the Kolmogorov-Zurbenko (KZ) filter was used for this type of trend separation. Moreover, multiple nonlinear regression analysis was used to find relationships between the PM10 concentration and several meteorological parameters. The goal of this analysis is to estimate the expected daily mean PM10 concentration values. The results of this analysis demonstrate that the regression equation can provide an adequate method for PM pollution forecasting. In addition to the hourly PM10 concentrations and basic meteorological data, global radiation and boundary layer height were considered in the characterization process.

Enhancing air quality forecasts by geomatic downscaling: an application to daily PM10 concentrations in France

Public health institutions need high-resolution next-day forecasts so they can order appropriate measures when there is a risk of air pollution exceeding regulatory thresholds. The MOCAGE model, the chemistry transport model developed by Meteo-France, forecasts hourly surface PM10 concentrations at a resolution of 0.1° throughout France (7.6 km). To obtain more efficient forecasts, a downscaling method is applied using topographic data (250-m resolution) and inventory data (2.2 km). All these disparate inputs are spatially standardized in a geographical information system to construct continuous daily fields at 250-m resolution. This method is suitable for large territories with widely varying environments (mountains, lowlands, coastlinessnap, urban areas, etc.) and areas with a low density of monitoring stations. The parameters used to improve MOCAGE forecasts are derived from “global” and “local” regressions describing the links between the daily PM10 concentration averages collected at 325 monitoring stations and seven explanatory variables (three topographic and four emission-inventory variables). One of the main results shows that the topographic and emission variables, respectively, explain 6% and 13% of PM10 variance in France. Analysis by local regression accounts for 74% of the spatial variation of PM10 concentration, while the global regression accounts for 49%. The results show above all that if the authorities responsible for human health protection had used the downscaling method instead of MOCAGE raw forecasts in 2016, they would have informed or alerted ten times as many people about the information and recommendation threshold (50 μg m−3) and alert threshold (80 μg m−3) being exceeded.

DEGRADATION OF AIR QUALITY (PM10) WITH SEASONAL CHANGE AND HEALTH RISK ASSESSMENT IN METRO CITY KOLKATA

Over the last few years, the whole nation is consistently facing a severe level of air pollution during the winter season. Air pollution level in all over India has started to get aggravated during the post-monsoon season due to an association of both atmospheric and anthropogenic factors. PM10. i.e. inhalable particles with a diameter less than or equal 10 micrometres, is a criteria air pollutant out of total twelve in India having great importance. In this study, seasonal variation of PM10 concentration was measured near Jadavpur, Kolkata from December 2017 to March 2018. During the study period results of ambient air quality monitoring data for the average concentration of PM10 ranged between 103.67 – 323.70 µg/m3. It is observed that in 100% of the cases, the 24hr average concentration of PM10 contravened the NAAQS (100 μg/m3) limit. The maximum concentration of PM10 was observed during the winter season (238.81±63.12 μg/m3) from January to February. To understand the existing air quality scenario, the Air quality index (AQI) value is computed based on PM10 concentration which is belonged to ‘Moderate’ to ‘Poor’ categories. It is observed that in December about 43% of days belong to the ‘poor’ category and 57% is of ‘moderate’, wherein the month of January only 25% days are a part of the moderate category, and the rest are of poor category. In the case of February month, only 17% of poor days are observed, and the air quality scenario gets improved with approaching summer season as in March air quality became moderate. Thus it is noticed that PM10 is a critical pollutant at this site in Kolkata. In this study, the number of higher rates of mortality and morbidity risk, respiratory diseases, cardiovascular diseases and chronic obstructive pulmonary disease risk for an exposed population is also estimated due to exposure to PM10.

Physical and chemical mechanisms of the daily-to-seasonal variation of PM10 in Korea

In this study, the CSEOF technique is used to investigate the physical and chemical mechanisms associated with the weekly PM10 variation in South Korea. For this end, 9 years of hourly measurements of PM10 in South Korea is used together with other gaseous contaminants (NO2, SO2, CO and O3) and traffic counts at the toll gates. The diurnal variation of PM10 concentrations indicates a significant correlation with human activities; higher concentrations in densely populated areas with a large volume of traffics and vice versa. In particular, the diurnal cycles of PM10, NO2 and CO show bimodal structures with maxima corresponding to the morning and evening rush hours. While NO2 and CO maxima are observed at ~9 AM, with some delay from the traffic maximum, PM10 peak is observed ~11 AM, indicating roughly 2 h of conversion process from gaseous pollutants to particulate matter. After the sunset, gas-to-particle conversion efficiency is significantly reduced and PM10 concentration begins to increase slowly. SO2 exhibits a slightly different feature, showing diurnal variation with a single peak at 11 AM and relatively minor contribution to the diurnal variation of PM10. O3 participates in the photochemical reactions of the gaseous pollutants, providing OH radical as an oxidant. Physical factors (e.g. boundary layer height, 10 m wind gust, wind convergence) affecting horizontal and vertical mixing of polluted air are responsible for dispersion of accumulated PM10 in the afternoon. As a result, strong morning-evening asymmetry is seen in the diurnal variation of PM10 concentrations. Also, notable seasonal dependency is observed in the concentrations of PM10 and the gaseous contaminants due to the seasonal variation of emission and the physical factors.

Temporal Assessment on Variation of PM10 Concentration in Kota Kinabalu using Principal Component Analysis and Fourier Analysis

PM10 (particulate matter with aerodynamic diameter below 10 microns) has always caught scientific attention due to its effect to human health. Predicting PM10 concentration is essential for early preventive measures, especially for cities such as Kota Kinabalu. Temporal data clustering may enhance accuracy of prediction model by group data in time range. However, the necessity of temporal data clustering has yet to be studied in Kota Kinabalu. OBJECTIVE. This research is conducted to compare significance of meteorological and pollutant factors for PM10 variation in clustered and unclustered data. METHODOLOGY. This study is focused in Kota Kinabalu, Sabah. The data for meteorological factors (Ws, Wd, Hum, Temp) and pollutant factors (CO2, NO2, O3, SO2, PM10) from 2003 to 2012 provided by Department of Environment are used for this research. Missing data are imputed using nearest neighbour method before it is clustered by monsoonal clustering. Unclustered and clustered datasets are analysed using principal component analysis (PCA) to check significance of factors contributing to PM10 concentration. FINDINGS. PCA results show that temporal clustering does not have noticeable effect on the variation of PM10 concentration. For all datasets, humidity and x-component wind speed have highest factor loading on PC1 and PC2 respectively. Further statistical analysis by 2-D regression shows that humidity (ρ = -0.60 ± 0.20) and temperature (ρ = 0.63 ± 0.11) have moderate to strong correlation towards PM10 concentration. This may be due to high humidity level and strong negative correlation between temperature and humidity (ρ = -0.91 ± 0.03). In contrast, both x- and y-component wind speed generally show weak correlation towards PM10, with ρ value of 0.09 ± 0.14 and 0.24 ± 0.18 respectively probably because of varying direction of particle dispersion. Fourier analysis further confirms this result by showing that human activity contributes major effect to variation of PM10 concentration.

The Influence of Evapotranspiration and Wet Deposition on the Variations of PM10 Concentration in the Ciuc Basin

Abstract Trend analysis for potential evapotranspiration (PET) and climatic water balance (CWB) is critical in identifying the particulate matter concentration (PM10) variations. The PET is computed based on the monthly average temperature for the Ciuc basin using Thornthwaite parameterization. The highest levels of evapotranspiration appear during the months of May and June. The lowest levels of particulate concentration characterize the period during April-June. Precipitation is highest during May and June. Particulate matter in the highest cloud water is 0.014 µg/m3/mm during April and 0.010 µg/m3/mm during May. One can observe a significant level of negative correlation between particulate matter concentration, the potential evapotranspiration and precipitation.

Effect of Meteorological Factors on PM<sub>10</sub> Concentration in Hanoi, Vietnam

According to the Ministry of Natural Resources and Environment, Hanoi city is currently facing the increased air pollution, especially particulate matter. This study examines the influence of meteorological factors including temperature, humidity and wind speed on PM10 concentration in Hanoi. The data used in this study include 24-h PM10 concentration data, hourly temperature, humidity and wind speed data in 2018 at three automatic air monitoring stations under Vietnam Environment Administration and Hanoi Natural Resources and Environment Department. The SPSS statistical analysis tools are utilized to analyze the correlation between PM10 concentration and meteorological factors through the Spearman (r) correlation coefficient. Additionally, the Independent-Sample T-Test is also employed to assess the difference in PM10 concentration in the period between dry season (from October to March) and wet season (from May to September). The results show that there is a trend of seasonal variation of PM10 concentration. Specifically, PM10 concentration recorded a higher value in winter and lower in summer. Different correlations between air pollutants and meteorological factors were also observed. PM10 concentration was inversely correlated (r 10 concentration during dry and wetseason. This outcome has confirmed the important role of meteorological factors in the formation of air pollution with large variations in different seasons and geological areas.

Photochemical reaction playing a key role in particulate matter pollution over Central France: Insight from the aerosol optical properties

Atmospheric particle is one of the major air pollutants, and believed to be important for air quality, radiative forcing and climate. Measurements of aerosol optical properties, size distribution and PM10 concentration were conducted at Orleans, central France during spring (7 March to 25 April) and autumn (25 October to 5 December) 2013. The average values of aerosol scattering coefficient (bsca), absorption coefficient (babs), single scattering albedo (SSA) at 532 nm and PM10 concentration are 54.9 ± 58.2 Mm−1, 10.6 ± 10.9 Mm−1, 0.81 ± 0.10 and 30.6 ± 21.6 μg/m3 for the spring campaign, and 35.4 ± 36.7 Mm−1, 3.9 ± 4.4 Mm−1, 0.83 ± 0.13 and 17.4 ± 11.8 μg/m3 for the autumn campaign, respectively. During the whole observation, the air parcel transported from Atlantic Ocean plays a role in cleaning up the ambient air in Orleans, while the air mass coming from the Eastern Europe induces the pollution events in Orleans. In this study, a simple approach, which based on the diurnal variation of PM10 concentration, Boundary layer depth (BLD) and the human activity factor derived from anthropogenic emission rate, was introduced to estimate the contribution of secondary aerosol to ambient aerosols. Our results show that secondary particles formation trigged by photochemical reactions and oxidations can contribute maximum of 64% and 32% for PM10 mass concentration during the spring and autumn time, respectively. These results highlight that photochemical reactions can enhance the atmospheric oxidation capacity and may faster the secondary particle formation and then play an important role in air quality.

A study of temporal variations of 7Be and 210Pb concentrations and their correlations with rainfall and other parameters in the South West Coast of India

As a part of establishing a regional database on natural radioactivity, the atmospheric concentrations of 210Pb and 7Be were measured over a three and half year period (2014–2017) in Mangalore and Kaiga in the South West Coast of India. A total of 99 air samples, collected in the different months of the year, were analysed in this study. The mean activity concentrations of 7Be and 210Pb were found to be 5.5 ± 3.1 mBq m−3 and 1.1 ± 0.73 mBq m−3, respectively. Both the radionuclides exhibited strong seasonal variations, with maximum concentration of 7Be occurring in the summer and that of 210Pb in the winter season. The concentration of both the radionuclides was minimum in the rainy season. Higher 210Pb concentration during winter was attributed to the ingression of continental air masses due to the wind regime from the North East. The sunspot number index of the solar activity also plays an important role in the increase and decrease of 7Be concentration in the air. A clear trend of increased and lowered concentration of 7Be with lower and higher solar activity (low and high sunspot number), respectively, in accordance with the 11-year solar cycle, was observed in this study. The temporal variation of PM10 concentration was also studied and it showed maximum value in the winter and minimum in the rainy season with an average of 56.9 μg m−3. Statistically significant positive correlation was observed between the PM10 and 210Pb activity concentration, whereas a weak correlation was observed between PM10 and 7Be. This is due to the fact that 7Be is largely associated with sub-micrometer size particles, whereas PM10 is contributed by larger sizes. The dependence of the activity concentrations of 7Be and 210Pb with meteorological parameters such as rainfall, temperature, and humidity was studied through linear regression analysis. A significant correlation was observed between 7Be and 210Pb concentrations with rainfall intensity (with identical correlation coefficients), which suggested that the removal mechanisms of these two radionuclides were similar. 7Be showed a strong correlation with temperature, whereas 210Pb with humidity. A comparison of the data obtained in the present study for the South West Coast of India with the global literature values of 7Be and 210Pb in aerosols showed that the values did not reflect the well-known latitudinal dependence of the 7Be tropospheric fluxes. Overall, the study provides an improved understanding of the correlation and variability of 210Pb and 7Be concentrations in the atmosphere in the South West Coast of India.

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.

Temporal Variation of Ambient PM10 Concentration within an Urban-Industrial Environment

PM10 concentration in the ambient air has been reported to be the main pollutant affecting human health, particularly in the urban areas. This research is conducted to study the variation of PM10 concentration at the three urban-industrial areas in Malaysia, namely Shah Alam, Kuala Terengganu and Melaka. In addition, the association and correlation between PM10 concentration and other air pollutants will be distinguished. Five years interval dataset (2008-2012) consisting of PM10, SOX, NOX and O3 concentrations and other weather parameters such as wind speed, humidity and temperature were obtained from Department of Environment, Malaysia. Shah Alam shows the highest average of PM10 concentration with the value of 62.76 μg/m3 in June, whereas for Kuala Terengganu was 59.29 μg/m3 in February and 46.61 μg/m3 in August for Melaka. Two peaks were observed from the time series plot using the averaged monthly PM10 concentration. First peak occurs when PM10 concentration rises from January to February and the second peak is reached in June and remain high for the next two consecutive months for Shah Alam and Kuala Terengganu. Meanwhile the second peak for Melaka is only achieved in August as a result of the transboundary of smoke from forest fires in the Sumatra region during dry season from May to September. Both of the pollutants can be sourced from rapid industrial activities at Shah Alam. PM10 concentration is strongly correlated with carbon monoxide concentration in Kuala Terengganu and Melaka with value of r2 = 0.1725 and 0.2744 respectively. High carbon monoxide and PM10 concentration are associated with burning of fossil fuel from increased number of vehicles at these areas.

Variations of PM10 concentration in Seoul during 2015 and relationships to weather condition

Variations of PM10 concentration in Seoul during 2015 and relationships to weather condition