Average Daily Concentrations Of PM10 Research Articles

Exposure to PM2.5 and PM10 pollution and the risk of respiratory diseases in upper Silesia inhabitants

Background: Multiple studies confirm that PM2.5 and PM10 can affect lung growth and development in children and adolescents, the number of medical visits, and hospital emergency admissions due to asthma, respiratory symptoms, and upper and lower respiratory tract disorders. Objective: The objective of this study was the measurement-based assessment for determining whether the concentrations of PM10 and PM2.5 are within admissible limits or exceeded in the Upper Silesia urban area (Silesia province) and to examine the relationship between PM concentrations and the number of admissions in Primary Health Care due to respiratory diseases like asthma and chronic bronchitis for children, adolescents and adults. Material and methods: The data provided by the Voivodship Inspectorate for Environmental Protection in Katowice, National Health Fund - Silesian Department, and the District Sanitary-Epidemiological Station in Katowice for the period 2012-2017 was used in the statistical analysis. Results: The increase in the number of excessive levels of average daily PM10 concentration was observed in almost all measurement stations, both for the acceptable level, information, and alarm level. The increase in average annual PM2.5 concentrations was also observed. The results showed an increased prevalence of respiratory diseases (ICD-Diagnosis Code 10-CM: J017, J030, J034, J038, J040) during the study period in 14 of 20 cities of Silesia province, consistently higher for children than adults. Conclusions: In Silesia province, the air quality is poor and has deteriorated over the last few years. Exposure to ambient PM2.5 and PM10 was significantly associated with the development of respiratory tract diseases in children and adults.

Spatial and Temporal Volatility of PM2.5, PM10 and PM10-Bound B[a]P Concentrations and Assessment of the Exposure of the Population of Silesia in 2018-2021.

Air pollution both indoors and outdoors is a major cause of various diseases and premature deaths. Negative health effects are more frequently observed in a number of European countries characterized by significant pollution. In Poland, especially in Upper Silesia, the most serious problem is the high concentration of particulate matter (PM) and PM10-bound benzo[a]pyrene (B[a]P). The main source of these two pollutants is so-called "low emissions" associated with the burning of solid fuels mainly in domestic boilers and liquid fuels in road traffic. This study examined the variability in the PM and PM10-bound B[a]P concentrations and their relationships with meteorological parameters, i.e., atmospheric pressure, air temperature and wind speed, in 2018-2021 at 11 monitoring stations. In many Silesian cities, the average annual concentrations of PM10, PM2.5 and B[a]P were much higher than those recorded in other European countries. At each station, the average daily PM10 concentrations were exceeded on 12 to 126 days a year. Taking into account the WHO recommendation for PM2.5, the highest recorded average daily concentration exceeded the permissible level by almost 40 times. The same relationships were observed in all measurement years: PM10 concentrations were negatively correlated with air temperature (R = -0.386) and wind speed (R = -0.614). The highest concentrations were observed in the temperature range from -15 °C to -5 °C, when the wind speed did not exceed 0.5 m·s-1. The calculated lifetime cancer risk (LCR) associated with the exposure to B[a]P in the Silesian Voivodeship suggested 30-429 cases per 1 million people in the heating season depending on the scenario used for the calculations (IRIS, EPA or WHO).

Mortality due to COVID-19 in Spain and its association with environmental factors and determinants of health

BackgroundThe objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 (CM) per 1,000,000 inhabitants as dependent variables, and average daily concentrations of PM10 and NO2 as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). The GLM model controlled for trend, seasonalities and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 g/m3 in PM10 and NO2 and by 1 ℃ in the case of Tmax and 1 g/m3 in the case of HA. Later, a linear regression was carried out that included the social determinants of health.ResultsStatistically significant associations were found between PM10, NO2 and the CM. These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM10 being the variable that showed greater association, with the CM followed of NO2 in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior.ConclusionsThe role of PM10 is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with CM. The role of the meteorological variables—temperature and HA—was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.

Short-term influence of environmental factors and social variables COVID-19 disease in Spain during first wave (Feb-May 2020).

This study aims to identify the combined role of environmental pollutants and atmospheric variables at short term on the rate of incidence (TIC) and on the hospital admission rate (TIHC) due to COVID-19 disease in Spain. This study used information from 41 of the 52 provinces of Spain (from Feb. 1, 2021 to May 31, 2021). Using TIC and TIHC as dependent variables, and average daily concentrations of PM10 and NO2 as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). Generalized linear models (GLM) with Poisson link were carried out for each provinces The GLM model controlled for trend, seasonalities, and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 μg/m3 in PM10 and NO2 and by 1 °C in the case of Tmax and 1 g/m3 in the case of HA. Later, a linear regression was carried out that included the social determinants of health. Statistically significant associations were found between PM10, NO2, and the rate of COVID-19 incidence. NO2 was the variable that showed greater association, both for TIC as well as for TIHC in the majority of provinces. Temperature and HA do not seem to have played an important role. The geographic distribution of RR in the studied provinces was very much heterogeneous. Some of the health determinants considered, including income per capita, presence of airports, average number of diesel cars per inhabitant, average number of nursing personnel, and homes under 30 m2 could explain the differential geographic behavior. As findings indicates, environmental factors only could modulate the incidence and severity of COVID-19. Moreover, the social determinants and public health measures could explain some patterns of geographically distribution founded.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19232-9.

Development, progression, and impact on urban air quality of the dust storm in Asia in March 15–18, 2021

An intense dust storm on 15–18 March 2021 that originated from the central and southern parts of the Gobi Desert in Mongolia affected most of northern, central, and eastern China. The dust storm had a significant impact on air quality over an area of more than 1.4 million km2 and threatened the lives of 350 million people. The analysis of the storm provided here is based on satellite, ground-based observations and reanalysis data. Satellite measurements showed that the maximum aerosol optical depth (AOD) values ranged from 2.4 to 4.5. In 42 Chinese cities, the mass average daily PM10 concentrations exceeded 1000 μg/m3 and the atmospheric visibility was reduced to 500–1000 m. Ordos and Jiayuguan had the highest hourly average PM10 concentrations at over 9999 μg/m3, the maximum value for measurements. Copernicus Atmospheric Monitoring Service (CAMS) reanalysis showed maximum dust AOD values of 2.4. Data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) found dust aerosols at an altitude of up to 12 km, and in many areas, the dust was found at an altitude between 0 and 4 km. A strong dust storm can result in a large amount of mineral aerosols of different sizes in the atmosphere that can significantly increase the heating rate of the atmosphere, subsequently affecting the regional dynamics of the atmosphere and the climate system as a whole.

The Impact of COVID-19 Lockdowns on Particulate Matter Emissions in Lombardy and Italian Citizens' Consumption Habits

Italy has been one of the first nations in the world to be heavily affected by COVID-19. A wide range of containment measures has been adopted from February to December 2020 to mitigate the pandemic. In this regard, the present research sets out to evaluate two aspects: (i) the impact of lockdowns on the concentrations of particulate matter (PM) 10 and 2.5 in the Lombardy region, and (ii) how anti-COVID-19 restrictions influenced Italian citizens' consumption habits. To do this, the average daily concentrations of PM10 and PM2.5 during 2020 in all the provinces of Lombardy were compared with those of the previous years through Welch's t-test. The same procedure was adopted to estimate the change in Google relative search volumes of home delivery services and smart working on a national scale. Two mean values were considered statistically confident when t < 1.5, suspiciously non-confident when 1.5 ≤ t < 1.9, and non-confident when t ≥ 1.9. Seasonalities and trends were assessed both graphically and with Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin tests. Finally, Pearson and Spearman correlations between changes in citizens' behavior and specific key events related to COVID-19 have been dealt with. The P-value threshold was indicatively set at 0.05. Microsoft Excel 2020 and Google Sheets were used as data analysis software. This paper showed: (i) the limited or insufficient effectiveness of lockdowns in reducing PM10 and PM2.5 concentrations in Lombardy, and (ii) a significant change in the consumption habits of Italian citizens, thus leading to both positive and negative results in terms of sustainability. Therefore, it is high time that both Italian and international environmental protection authorities thoroughly investigated the role of non-mobility-related sources of particulate emissions to impose effective rules on home delivery services. Moreover, further research is required for the understanding of anthropogenic, environmental, and atmospheric phenomena that influence the concentrations of PM10 and PM2.5.

Impact of environmental factors and Sahara dust intrusions on incidence and severity of COVID-19 disease in Spain. Effect in the first and second pandemic waves.

Scientific evidence suggests that Saharan dust intrusions in Southern Europe contribute to the worsening of multiple pathologies and increase the concentrations of particulate matter (PM) and other pollutants. However, few studies have examined whether Saharan dust intrusions influence the incidence and severity of COVID-19 cases. To address this question, in this study we carried out generalized linear models with Poisson link between incidence rates and daily hospital admissions and average daily concentrations of PM10, NO2, and O3 in nine Spanish regions for the period from February 1, 2020 to December 31, 2020. The models were adjusted by maximum daily temperature and average daily absolute humidity. Furthermore, we controlled for trend, seasonality, and the autoregressive nature of the series. The variable relating to Saharan dust intrusions was introduced using a dichotomous variable, NAF, averaged across daily lags in ranges of 0–7 days, 8–14 days, 14–21 days, and 22–28 days. The results obtained in this study suggest that chemical air pollutants, and especially NO2, are related to the incidence and severity of COVID-19 in Spain. Furthermore, Saharan dust intrusions have an additional effect beyond what is attributable to the variation in air pollution; they are related, in different lags, to both the incidence and hospital admissions rates for COVID-19. These results serve to support public health measures that minimize population exposure on days with particulate matter advection from the Sahara.

Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010-2017 in South Africa.

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.

Assessment of global and regional PM10 CAMSRA data: comparison to observed data in Morocco.

Given the strong impact of air quality on health, environment, and economy, Morocco has implemented an air quality network to assess air pollutants including PM10 (particulate matter with a diameter less than 10 μm). This network which is composed of 29 fixed measurement stations is spatially limited and does not provide sufficient time resolution. The scarcity of measured air quality data led to seek an optimal alternative source to conduct related data-based studies. This represents the primary objective of this paper. PM10 concentrations of global Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) data (4D Variational analysis "4v" and analysis "an"), as well as regional CAMSRA data, were examined against the average daily PM10 concentrations collected from six fixed Moroccan air quality measurement stations in 2016 (i.e., observation data). The verification is carried out by studying and analyzing seasonal, extreme, and annual values. The study shows a strong seasonal dependence with a positive bias in winter and a negative bias during summer. For the study of extreme values, global CAMSRA "an" and "4v" data record significant bias of approximately 184 and 161 μg/m3, respectively. However, the annual analysis shows that the CAMSRA global "an" data have the smallest average bias (20.008 μg/m3) and hence has the closest representation of observation data. We conclude that the CAMSRA global analysis data could be used to compute climatology, study trends, evaluate models, benchmark other reanalysis, or serve as boundary conditions for regional models for past periods.

Short-term effects of Saharan dust intrusions and biomass combustion on birth outcomes in Spain

The objective of this study is to analyze the short-term effects of atmospheric pollutant concentrations (PM10, NO2 and O3) and heat and cold waves on the number of pre-term births and cases of low birth weight related to Saharan dust advection and biomass combustion. The dependent variables used in this analysis were the total number of births, births with low weight (>2.500 g) and pre-term births (<37 weeks), that occurred at the province level. Data provided by the NSI included: days with Saharan dust intrusion or biomass advection classified in terms of information provided by MITECO for each of the nine regions in Spain. A representative city was selected for reach region in which the registered average daily concentrations of PM10, NO2 and O3 (μg/m3) were used. These were also provided by MITECO. The daily maximum and daily minimum temperature (°C) used was those registered by the meteorological observatory station located in each province capital, provided by AEMET. Using Poisson log linear regression models, the associated relative risks (RR) were measured as well as the population attributable risk (PAR) corresponding to the variables that resulted statistically significant at p < 0.05 for days with and without intrusion of natural particulate matter.The results obtained show that the days with Saharan dust intrusion or advections due to biomass combustion- beyond the impact of PM10, primary pollutants such as NO2 (in Saharan intrusions), heat waves and O3 - are associated with the number of births, low birth weight and pre-term birth. The RR and percent PAR of the pollutants and the heat waves are greater than those obtained for PM10.The results of this study indicate that days with natural particulate matter due to biomass combustion or advection of Saharan dust put pregnant women at risk.

Assessment of Air Pollution Aggravation during Straw Burning in Hubei, Central China.

Crop straw burning frequently occurs in Central China, where agriculture is highly productive. We carried out a two-month observation on straw burning in Hubei Province from September 1 to October 31, 2015 to track the variations of air pollutants and comprehensively quantify their influence on regional air quality. Results showed that the concentration of suspended particles (particles smaller than 2.5 or 10 µm, i.e., PM2.5/PM10) and gas pollutants including ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) was significantly enhanced with the increasing number of fire spots. The average daily concentrations of PM10, PM2.5 and O3 during the intensive burning period (from October 12 to 25) exceeded the daily limits published by the World Health Organization (WHO) by 101.8, 72.7 and 59.1 μg/m3, respectively. In the hourly distribution of pollutant concentration, PM10, PM2.5, O3, SO2, NO2 and CO were 63.49%, 46.29%, 65.56%, 64.40%, 48.57% and 13.49% higher during burning periods than during non-burning periods. Statistical results based on the air quality index (AQI) indicated that biomass burning was the key factor for the deterioration of local air quality, with a contribution ratio exceeding 41%. Additionally, the pollutants were more spatially homogeneous during the burning period than during the non-burning period. Straw burning not only worsened the local air quality but also raised the pollution level of surrounding regions due to the transport of air mass.

Cardiovascular and respiratory emergency dispatch due to short-term exposure to ambient PM10 in Dezful, Iran.

Introduction: This study was conducted to determine the relation between exposure to particulate matter less than 10 microns (PM10) caused by dust storms and the risk of cardiovascular, respiratory and traffic accident missions carried out by Emergency Medical Services (EMS). Methods: This was a time-series study conducted in Dezful city, Iran. Daily information on the number of missions by the EMS due to cardiovascular, respiratory and crash problems and data on PM10 were inquired from March 2013 until March 2016. A generalized linear model (GLM) with distributed lag models (DLMs) was used to evaluate the relation between the number of EMS missions and the average daily PM10. The latent effects of PM10 were estimated in single and cumulative lags, up to 14 days. Results: In the adjusted model, for each IQR increase in the average daily PM10 concentration, the risk of EMS missions in the total population in single lags of 2 to 7 days, and the cumulative lags of 0-7 and 0-14 days after exposure had a 0.8, 0.8, 0.8, 0.8, 0.7, 0.6, 6.7 and 1.4% significant increase. Also, for each IQR increase in the daily mean concentration of PM10 in single 1 to 7, and cumulative lags of 0-2, 0-7, and 0-14 days after exposure, respectively, a 2.4, 2.7, 2.8, 2.9, 2.9, 2.7, 2.5, 7.4, 23.5 and 33. 3 % increase was observed in the risk of EMS cardiovascular missions. Conclusion: Increase in daily PM10 concentrations in Dezful is associated with an increase in the risk of EMS missions in lags up to two weeks after exposure.

Characteristics of air quality and sources affecting high levels of PM10 and PM2.5 in Poland, Upper Silesia urban area

The reports concerning air quality published by WHO and EEA showed that 33 out of 50 cities with highest concentration of particulate matter (PM)2.5 in UE are located in Poland. Various investigations identify main health outcomes to be consistently related to PM10 and PM2.5. Increased concentration of PM is responsible for 47.3 thousands of premature deaths every year in Poland. The objective of this study was the measurement-based assessment for determining whether the concentrations of PM10 and PM2.5 are within admissible limits or exceeded in Silesia Province. The data provided by the Voivodship Inspectorate for Environmental Protection in Katowice was used in the analysis. The measurements were made in years 2009–2016 for PM2.5 and 2014–2017 for PM10 in three measurement stations: two in Katowice (capital of Silesia Province) and one in Żory. The increase in the number of excessive levels of average daily PM10 concentration in year 2017 were observed in all three measurement stations, both for the acceptable level, information and alarm level, with lack or singular excessive levels in the previous years. The increase in average annual PM2.5 concentrations in year 2016 was also observed, as compared to the previous year in all three measurement stations. The highest pollution is observed in winter. The main cause of exceeded acceptable PM concentrations in Poland is household heating systems, boilers and furnaces burnt with coal or wood, and chimneys. In Silesian Province, the air quality is poor and has deteriorated over the last year.

Temperature modulation of the health effects of particulate matter in Beijing, China.

Particulate matter (PM) has been proven to cause health risks and may result in hospital emergency room visits (ERVs), which might be complicated by extreme temperature events. However, it remains unclear how temperature modulates the effect of different-sized particles on ERVs. This study used three separate time series analyses (2009-2011) to explore such temperature modulation effect in Beijing, China. The analytical approaches included a bivariate response surface model, a non-stratification parametric model, and a stratification parametric model. Results showed that the average daily concentrations of PM10 and PM2.5 in Beijing were 110.16 and 67.89μg/m3, respectively, during the study period, which were higher than in most Western countries. Our findings indicated that the temperature modulation effects of PM2.5 were more evident than that of PM10. The effects of PM on morbidity depend on temperature. The effects were estimated for the increases in total, respiratory, and cardiovascular ERVs per 10μg/m3 increase in PM2.5 and PM10 concentrations at high temperature level (> 28°C). The estimated increases in the three types of ERVs for PM2.5 were 0.15, 0.35, and 0.34%, respectively. For PM10, the increases were 0.12, 0.08, and 0.14%, respectively. In addition, the results showed that the elderly (age ≥ 65) and women are more vulnerable to PM at high temperatures. These findings may have implications for the health impact associated with both air pollution and global climate change.

A case study of potential human health impacts from petroleum coke transfer facilities

ABSTRACTPetroleum coke or “petcoke” is a solid material created during petroleum refinement and is distributed via transfer facilities that may be located in densely populated areas. The health impacts from petcoke exposure to residents living in proximity to such facilities were evaluated for a petcoke transfer facilities located in Chicago, Illinois. Site-specific, margin of safety (MOS) and margin of exposure (MOE) analyses were conducted using estimated airborne and dermal exposures. The exposure assessment was based on a combined measurement and modeling program that included multiyear on-site air monitoring, air dispersion modeling, and analyses of soil and surfaces in residential areas adjacent to two petcoke transfer facilities located in industrial areas. Airborne particulate matter less than 10 microns (PM10) were used as a marker for petcoke. Based on daily fence line monitoring, the average daily PM10 concentration at the KCBX Terminals measured on-site was 32 μg/m3, with 89% of 24-hr average PM10 concentrations below 50 μg/m3 and 99% below 100 μg/m3. A dispersion model estimated that the emission sources at the KCBX Terminals produced peak PM10 levels attributed to the petcoke facility at the most highly impacted residence of 11 μg/m3 on an annual average basis and 54 μg/m3 on 24-hr average basis. Chemical indicators of petcoke in soil and surface samples collected from residential neighborhoods adjacent to the facilities were equivalent to levels in corresponding samples collected at reference locations elsewhere in Chicago, a finding that is consistent with limited potential for off-site exposure indicated by the fence line monitoring and air dispersion modeling. The MOE based upon dispersion model estimates ranged from 800 to 900 for potential inhalation, the primary route of concern for particulate matter. This indicates a low likelihood of adverse health effects in the surrounding community. Implications: Handling of petroleum coke at bulk material transfer facilities has been identified as a concern for the public health of surrounding populations. The current assessment, based on measurements and modeling of two facilities located in a densely populated urban area, indicates that petcoke transport and accumulation in off-site locations is minimal. In addition, estimated human exposures, if any, are well below levels that could be anticipated to produce adverse health effects in the general population.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter <10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2 = 66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance.Implications: The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

A methodology for the determination of fugitive dust emissions from landfill sites

This study focuses on the development of a methodology for the determination of the contribution of fugitive dust emissions from landfill sites to ambient PM10 concentrations and the subsequent exposure to working personnel. Fugitive dust emissions in landfills mainly originate from resuspension due to truck traffic on paved and unpaved roads and from wind-blown dust from landfill cover soil. The results revealed that exposure to PM10, originating from fugitive dust emissions in the landfill site, was exceeding the health protection standards (50 μg m−3). The higher average daily PM10 concentration (average value) for weekdays was equal to 275 μg m−3 and was computed for the areas nearby the unpaved road located inside the landfill facilities that lead to the landfill cell. The percentage contributions of road and wind-blown dust to the PM10 concentrations on weekdays were equal to 76 and 1 %, respectively. The influence of the background concentration is estimated close to 23 %.

Mass concentrations and indoor-outdoor relationships of PM in selected educational buildings in Nis, Serbia

Mass concentrations of particulate matter (PM) fractions were measured in educational buildings in the city of Nis, Republic of Serbia. Two sampling campaigns were conducted in winter periods. The first campaign was in the period from 21 February to 15 April 2010 at the Faculty of Occupational Safety (FOS) and the second campaign was from 20 March to 4 April 2013 at the primary school Vozd Karadjordje (VK). PM measurements were carried out with low volume samplers Sven/Leckel LVS3. The average daily PM10 concentration inside the FOS (47.0 ?21.8 ?g/m3) was lower than PM10 concentration in outdoor air (50.7 ?28.1 ?g/m3). The average daily PM10 concentration inside the VK (54.6 ? 17.6 ?g/m3) was higher than in outdoor air (47.9 ? 22.8 ?g/m3). The 24 hours average PM10 concentrations at FOS exceeded the EU limit value (50 mg/m3) during 34 % of days outdoors, and 39 % of days indoors. The 24 hours average PM10 concentrations at VK exceeded the limit value during 35 % of days outdoors, and 53 % of days indoors. The 24 hours average PM2.5 concentrations at VK exceeded the WHO daily mean guideline value (25 mg/m3) during 71 % of days outdoors, and 88 % of days indoors. The average PM10 I/O ratio at VK was 1.57 during teaching hours, and 1.00 during no teaching hours. Similarly, average PM2.5 I/O ratio at VK was 1.11 during teaching hours and 0.90 during no teaching hours. Average daily PM2.5/PM10 ratio in the ambient air at VK was 0.87, and 0.82 at FOS. Very strong correlations between the indoor and outdoor PM concentrations were observed at VK during no teaching hours (r&gt;0.8). Moderate to strong negative correlations were found between the wind speed and PM at both schools. High outdoor PM concentrations and resuspension of particles are probably the most possible reasons for the elevated indoor PM concentrations found in the study.

PM10 concentration levels at an urban and background site in Cyprus: The impact of urban sources and dust storms

Air quality in Cyprus is influenced by both local and transported pollution, including desert dust storms. We examined PM10 concentration data collected in Nicosia (urban representative) from April 1, 1993, through December 11, 2008, and in Ayia Marina (rural background representative) from January 1, 1999, through December 31, 2008. Measurements were conducted using a Tapered Element Oscillating Micro-balance (TEOM). PM10 concentrations, meteorological records, and satellite data were used to identify dust storm days. We investigated long-term trends using a Generalized Additive Model (GAM) after controlling for day of week, month, temperature, wind speed, and relative humidity. In Nicosia, annual PM10 concentrations ranged from 50.4 to 63.8 μg/m3 and exceeded the EU annual standard limit enacted in 2005 of 40 μg/m3 every year. A large, statistically significant impact of urban sources (defined as the difference between urban and background levels) was seen in Nicosia over the period 2000–2008, and was highest during traffic hours, weekdays, cold months, and low wind conditions. Our estimate of the mean (standard error) contribution of urban sources to the daily ambient PM10 was 24.0 (0.4) μg/m3. The study of yearly trends showed that PM10 levels in Nicosia decreased from 59.4 μg/m3 in 1993 to 49.0 μg/m3 in 2008, probably in part as a result of traffic emission control policies in Cyprus. In Ayia Marina, annual concentrations ranged from 27.3 to 35.6 μg/m3, and no obvious time trends were observed. The levels measured at the Cyprus background site are comparable to background concentrations reported in other Eastern Mediterranean countries. Average daily PM10 concentrations during desert dust storms were around 100 μg/m3 since 2000 and much higher in earlier years. Despite the large impact of dust storms and their increasing frequency over time, dust storms were responsible for a small fraction of the exceedances of the daily PM10 limit.ImplicationsThis paper examines PM10 concentrations in Nicosia, Cyprus, from 1993 to 2008. The decrease in PM10 levels in Nicosia suggests that the implementation of traffic emission control policies in Cyprus has been effective. However, particle levels still exceeded the European Union annual standard, and dust storms were responsible for a small fraction of the daily PM10 limit exceedances. Other natural particles that are not assessed in this study, such as resuspended soil and sea salt, may be responsible in part for the high particle levels.

Seasonal variation and source apportionment of organic tracers in PM10 in Chengdu, China.

Organic compound tracers including n-alkanes, triterpane, sterane, polycyclic aromatic hydrocarbons (PAHs) and dicarboxylic acids of airborne particulate matter (PM10) were characterized for samples collected at five sites from July 2010 to March 2011 using GC/MS. Spatial and temporal variations of these organic tracers in PM10 were studied, and their sources were then identified respectively. Average daily concentrations of PM10 varied in different seasons with the trend of PM10 in winter (0.133 mg/m(3)) > autumn (0.120 mg/m(3)) > spring (0.103 mg/m(3)) > summer (0.098 mg/m(3)). Daily concentrations of n-alkanes (C11-C36) ranged from 12.11 to 163.58 ng/m(3) with a mean of 61.99 ng/m(3). The C max and CPI index of n-alkanes indicated that vehicle emissions were the major source in winter, while the contributions of high plant wax emissions became significant in other seasons. It was discovered that the main sources of triterpenoid and steranes were gasoline and diesel engine emissions. Concentrations of ∑15PAHs in PM10 also varied (12.25-58.56 ng/m(3)) in different seasons, and chrysene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(ghi) perylene and fluoranthene were the dominant components. In the four seasons, the concentration of ∑15PAHs was relatively higher at the northern site because of traffic congestion. The main source of airborne PAHs was traffic emissions and coal combustion. Average daily concentrations of dicarboxylic acids (C4-C10) in PM10 ranged from 12.11 to 163.58 ng/m(3), of which azeleic acid was the major compound (0.49-52.04 ng/m(3), average 14.93 ng/m(3)), followed by succinic acid (0.56-19.08 ng/m(3), average 6.84 ng/m(3)). The ratio of C6/C9 showed that the major source in winter was biological, while the contributions of emissions from anthropogenic activities were much higher in summer.