Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data (original) (raw)
Related papers
We have studied particulate matter (PM) concentrations, PM 10 and PM 2.5 , measured in an urban air quality monitoring network in the Helsinki Metropolitan Area during 1997–1999. The data includes PM 10 concentrations measured at five locations (two urban traffic, one suburban traffic, one urban background and one regional background site) and PM 2.5 concentrations measured at two locations (urban traffic and urban background sites). The concentrations of PM 10 show a clear diurnal variation, as well as a spatial variation within the area. By contrast, both the spatial and temporal variation of the PM 2.5 concentrations was moderate. We have analysed the evolution of urban PM concentrations in terms of the relevant meteorological parameters in the course of one selected peak pollution episode during 21–31 March, 1998. The meteorological variables considered included wind speed and direction, ambient temperature, precipitation, relative humidity, atmospheric pressure at the ground level, atmospheric stability and mixing height. The elevated PM concentrations during the 1998 March episode were clearly related to conditions of high atmospheric pressure, relatively low ambient temperatures and low wind speeds in predominantly stable atmospheric conditions. The results provide indirect evidence indicating that the PM 10 concentrations originate mainly from local vehicular traffic (direct emissions and resuspension), while the PM 2.5 concentrations are mostly of regionally and long-range transported origin.
Spatial Variation of PM10 in Turkey
Air pollution is a major environmental problem in the developing countries of the world with rapid growth of population, industrial activities, and traffic density. Turkey faces facing air pollution problems with all of them. This study describes analysis of particulate matter concentrations of the cities in Turkey. Hourly PM10 concentrations were used to calculate the seasonal and yearly spatial variation patterns of air pollutants in Turkey based on the data of 118 stations between the periods of 2008-2012. Spatial and seasonal variations of PM10 concentrations are fairly high over Turkey. In south eastern sites of Turkey, high concentration occurs in spring and summer, which could be attributed to long-range Saharan dust transportation and the transport of dust by southerly winds from the desert regions of Mesopotamia. A significant seasonal variation of PM10 concentrations observed in some cities in different regions is characterized by high concentrations in winter and low in s...
Atmospheric Environment, 2011
This paper analyzes PM10 concentration data collected at 10 stations for the period of 2005e2009 by Air Quality Section of Istanbul Municipality. The analysis has been focused on spatial and temporal variations of the pollutants and their possible sources in the urban zones of the city. PM10 concentrations in Istanbul show significant variations across the city, with current PM10 levels at several traffic hot points and industrial zones which exceed EC air quality limit. The general temporal pattern is characterized by high concentrations in winter and low in summer. The number of exceedences allowed by the EU was surpassed at all monitoring sites during the analyzed years which reflect the serious pollution problem in the city.
Inhalable particulate matter (PM10) has been monitored at several stations byIstanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 lg m3, was lower than the Turkish air qualitystandard of 60 lg m3. On the other hand, this value was found higher than the annual European Union air qualityPM 10 standard of 40 lg m3. Furthermore, the annual mean concentration of PM2.5 20.8 lg m3 was found higher than The United States EPA standard of 15 lg m3. The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequencydistribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov–Smirnov tests, the frequencydistributions of PM2.5 and PM10 data were found to fit Log-logistic functions.
14th SGEM GeoConference on ENERGY AND CLEAN TECHNOLOGIES, 2014
ABSTRACT The main aim of the paper was to explore the interrelationships between PM10 time series and various weather parameters i.e. air temperature, atmospheric pressure, relative humidity, solar radiation, and wind speed, recorded hourly at six automated monitoring stations located in Oltenia South-West region of Romania. Significant correlations (p<0.001) were observed between PM10 concentrations and air temperature, PM10 and atmospheric pressure, and PM10 and wind speed at all stations. These associations were not dependent on monitoring location, and provided reliable PM10 concentrations tendencies according to weather parameter′s change. PM10 concentrations increased with temperature abatement, increased with atmospheric pressure rising, and decreased with wind speed enhancement. A case study performed in Targoviste city is presented to supplement the knowledge on PM10 spatial variation. Resulted thematic maps with isolines showed high levels of PM10 particles in the western and northwest parts of the city, which are correlated with intense heavy traffic and neighboring active industries from northwest. Geo-referenced PM10 quantitative knowledge facilitates the selection of control strategies for reducing exposure risks for the inner-city residents.
Atmospheric Environment, 2008
In this study, we developed two methods to distinguish the long-range transport (LRT) episodes from local pollution (LP) episodes. The first method is based on particle number concentrations ratio between accumulation mode (diameter 490 nm) and Aitken mode (diameter 25-90 nm). The second method is based on a proxy variable (interpolated ion sum) for long-range transported PM 2.5 . The ion-sum is available from the measurements of sulphate, nitrate and ammonium at the nearest EMEP stations. We also utilised synoptic meteorological weather charts, locally measured meteorological data, and air mass back-trajectories to support the evaluation of these methods. We selected nine time periods (i.e. episodes) with daily average PM 10 450 mg m À3 in the Helsinki Metropolitan Area during year 2002. We characterized the episodes in terms of PM 10 and PM 2.5 concentrations and the fraction of fine particles in PM 10 at an urban traffic and regional background air quality monitoring sites. Three of these episodes were clearly of local origin. They were characterized by a low average fraction of PM 2.5 (o0.2) in PM 10 at the urban traffic monitoring site, low ratio between PM 10 concentrations at the regional background site and at the urban traffic site (o0.2), low average ion sums (1.5-2.5 mg m À3 ) and low accumulation to Aitken mode ratios (0.13-0.26). Four of the episodes had distinct LRT characteristics: a high fraction of fine particles in PM 10 (0.5-0.6) at the urban traffic site, a high ratio between PM 10 concentrations at the regional background site and at the urban traffic site (0.7-0.8), high interpolated values for the ion sum (6.6-11.9 mg m À3 ), and high accumulation to Aitken mode ratios (0.75-0.85). During the remaining two episodes there was significant contribution from both local sources and LRT. A detailed analysis of meteorological variables and air mass back-trajectories gave support to these findings. These characteristics can be utilised in a simple procedure to distinguish between LRT and LP episodes. Further quantitative investigations to these characteristics provide an indication to the episode strength. The quantitative results presented in the current study are applicable to the Helsinki Metropolitan Area and similar cities. Nevertheless, developing these methods for other cities require analyses of the meteorological conditions, behavior of the PM concentrations, and air-mass back trajectories for that specific city. r
Environmental Science and Pollution Research, 2020
The air quality decrease, especially in urban areas, is related to local-scale conditions and to dispersion of air pollutants (regional and long-range) as well. The main objective of this study was to decipher the seasonal variation of PM 10 , NO, NO 2 , NO x , SO 2 , O 3 , and CO over a 1-year period (2017) and the possible relationships between air pollution and meteorological variables. Furthermore, trajectory cluster analysis and concentration-weighted trajectory (CWT) methods were used to assess the trajectories and the sourcereceptor relationship of PM 10 in the Ciuc basin Transylvania, known as the "Cold Pole" of Romania. The pollutants show lower concentrations during warmer periods, especially during summer, and significantly higher concentrations were observed on heating season in winter due to seasonal variations in energy use (biomass burning) and atmospheric stability. Subsequently, in February, the highest concentration of PM 10 was 132 μg/m 3 , which is 4 times higher than the highest recorded monthly mean. Our results indicate a negative correlation between CO/temperature (− 0.89), NO x /temperature (− 0.84) and positive between NO x /PM 10 (0.95), CO/PM 10 (0.9), and NO x /CO (0.98), respectively. Dominant transport pathways were identified and the results revealed that slowmoving southerly (~45%) and northwesterly (~32%) air masses represent almost 80% and mainly regional flows were discerned. During 2017, increased PM 10 levels were measured at the study site when air masses arrived mostly from northwest and southeast. The CWT and polarplot models show a strong seasonal variation and significant differences were observed between weekdays and weekends, namely highest PM 10 concentrations during weekends at low wind speed (2-4 m/s).
Spatial analysis of air quality in Tehran with emphasis on particulate matter (PM 2.5 and PM 10
2022
Maryam Ansari, Mahmoud Ahmadi, Gholamreza Goudarzi Volume 11, Issue 32 - Serial Number 2 June 2022 Pages 109-128 Nowadays, the air of most of Iran's cities especially high population metropolises hasn’t an optimal quality. This adverse quality is due to various pollutant resources such as automobiles, industries, heating devices, construction, and commercial activities during recent decades and there are more concerns about it. Therefore, monitoring air pollutants and studying their seasonal and spatial variations are specifically important. The present study aimed to evaluate air quality and seasonal and spatial variations of particulate matter (PM2.5 and PM10) in Tehran city. In this research, the AQI index has been used to determine the air quality of Tehran and to introduce the responsible pollutant. To investigate the variations of particulate matter (PM10 and PM2.5) in seasonal and spatial scales, the data of air pollution monitoring stations (18 stations) of Air Quality Control Company was used in 2018 and 2019. Data were analyzed using Excel and SPSS software and results of statistical analysis of pollutants distribution in Temporal – spatial scales are provided and they are drawn using Arc GIS software and analytical function of inverse Distance Weighting interpolation (IDW) as maps, tables, and graphs. Based on the results, favorable and unfavorable air quality were respectively observed in 83.8 and 16.2% of days in 2018, as well as 76.4 and 23.6% of days in 2019, which can be related to the changes in rainfall rate and wind speed in the years. The maximum seasonal concentration of particulate matter (PM10 and PM2.5) relates to summer and winter respectively and the minimum seasonal concentration of both pollutants relates to spring. Results of inverse Distance Weighting interpolation (IDW) also showed that th