Concentrations Of Atmospheric Pollutants Research Articles

Simulation of Point Source Pollutant Dispersion Pattern: An Investigation of Effects of Prevailing Local Weather Conditions

This study investigated the potential effects of prevailing local atmospheric conditions on dispersion pattern of point source emissions from a Scrap-Iron and Steel Smelting Factory, Ile-Ife, Nigeria. The American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) was adopted to predict the atmospheric dispersion of particulate matter (PM) emissions around the source. The PM estimates at two experimental points (M1 and M2) around the source were compared with the observations. The model simulations suggested that the PM was mostly dispersed by the dominant southwesterly wind such that the plume moved towards the northeast with variations in their spatial distributions across the seasons. Under low wind speeds and humid conditions, there was accumulation of the pollutants within the periphery of the point source. The simulated pollutant concentrations compared fairly well with the observations for both fine PM, i.e., PM2.5 (mean error = − 6441 µg m−3, kappa coefficient, κ = 0.31 at M1 and mean error = − 16,608 µg m−3, κ = 0.41 at M2) and the coarse, i.e., PM2.5–10 (mean error = − 5506 µg m−3, κ = 0.27 at M1 and mean error = − 4174 µg m−3, κ = 0.36 at M2). A hypothetical increase in stack height enhanced effective plume rise which resulted in a decrease in atmospheric pollutant concentration. The study has implication in industrial air pollution reduction.

Correlation analysis of AQI characteristics and meteorological conditions in heating season

According to the China air quality monitoring platform, the monthly average of the Air Quality Index for monitoring the heating season in Xi’an and the monthly concentration of major atmospheric pollutants in the past three years, the relationship between five major atmospheric pollutants and meteorological conditions was analyzed, the main results are as follows: (1) in the past three years, the air quality in Xi’an heating season was mild pollution, and the most serious degree of pollution was in January each year. (2) The main pollutants in Xi’an were PM2.5 and PM10, but in recent years, the content of pollutants NO2 and O3 has risen sharply, so we should strengthen the protection and the treatment of NO2 and O3. (3) The precipitation has a certain impact on air quality index. (4) The air quality in Xi’an is closely related to meteorological elements.

Internal Gravity and Infrasound Waves during the Hurricane of May 29, 2017, in Moscow

Data on internal gravity and infrasound waves recorded during the passage of both warm and cold fronts throughout Moscow, which are associated with the atmospheric storm of May 29, 2017, are given. These waves were recorded by four microbarographs located in the city of Moscow and Moscow region (and marked IFA, MGU, MSR, and ZNS in Fig. 1) and the data obtained were compared with data on infrasound waves recorded at the IS43 station in the town of Dubna. Time variations in the characteristics of internal gravity and infrasound waves (coherence, propagation azimuths, phase velocities, characteristic periods, and frequency spectra) during the passage of both warm and cold fronts are studied. The transition from the gravity to acoustic dispersive branch of acoustic-gravity waves due to increasing frequency and the temporal modulation of the phase velocity of infrasound waves due to internal gravity waves (IGWs) are also studied. Data on both aerosol (PM10) and gas (NO2) concentrations measured at different Moscow stations during the approaching atmospheric storm are given. The possibility of detecting wave precursors of atmospheric storms in simultaneous variations in atmospheric pressure, wind velocity, and pollutant concentrations is studied.

Development of the method for rapid detection of hazardous atmospheric pollution of cities with the help of recurrence measures

The method for rapid detection of hazardous pollution of the atmosphere of cities, which is based on dynamic measures of recurrence (repeatability) of the states of the pollution concentration vector, was developed. The new scientific result is related to the use of the unconventional modification of the known measures of recurrence based on the dynamic window averaging the current recurrence of the states of atmospheric pollution concentration. One type of a window has the width that is increasing over actual time of measurements. The other type uses the window of a fixed width that is movable over the time of measurements. The modified measures take into consideration the integrated nature of explicit and hidden destabilizing factors that contribute to current pollution concentration at the point of control. In this case, it is emphasized that there is no need to take into consideration the traditional meteorological or other conditions when identifying hazardous pollution of the atmosphere. The developed method makes it possible to detect rapidly not only explicit, but also hidden dangerous pollutions of the air basin in cities and thus to improve the effectiveness and timeliness of the measures to reduce the harmful effects of pollution of the atmosphere on the population and the environment. Nitrogen dioxide was considered as a hazardous pollutant during the experimental verification of the method. It was established experimentally that the dynamics of the concentration of nitrogen dioxide in the atmosphere of a typical urban configuration has a fractal structure, which depends on the pollution control points. In this case, these structures are characterized by the existence of the elements of periodic and extreme topologies with sharp changes in dynamics. The modified measures were found to characterize the features of specific structures and to detect not only explicit, but also hidden hazards of atmosphere pollution. In this experiment, the dynamics of the modified measures varies from zero to 0.78 units. It was shown that the maximum value of the measures belongs to the interval of observation, which is determined by 12–36 counts. It was established that at the studied control points, current concentrations of nitrogen dioxide exceeded the limit concentrations by 2.75–4.5 times and admissible maximum single concentrations – by 1.3–2.1 times. It was determined that abrupt changes in the dynamics of the modified measures can serve as an indicator of not only explicit, but also hidden hazardous pollution of the atmosphere of cities

Application of degree-day factors for residential emission estimate and air quality forecasting

Results of air quality modelling are strongly influenced by the emission inventory used. This is related to spatial and temporal allocation of emission. In Poland, the share of residential combustion (RC) in total emission of particulate matter is large, and the temporal profile of emission from this sector is influenced by meteorological conditions. Here we show the performance of the WRF-Chem model forecasts, running for the area of Poland with two different approaches to temporal distribution of emission from RC. First, emissions are distributed temporally using the monthly, daily and hourly emission factors provided by GENEMIS project (base run). Second, the model uses the heating degree-day (HDD) approach RC. We show that both approaches result in similar model performance for PM10 and PM2.5. The HDD approach leads to better forecasts for the warm days, whereas the base run forecasts often show too high concentrations of atmospheric pollutants for such periods.

Association entre les niveaux de pollution atmosphérique et l’augmentation de la consommation médicamenteuse pour asthme et allergies dans 12 grandes villes de France métropolitaine, pour un total de 12 millions d’individus entre 2009 et 2015

IntroductionThe diversity of epidemiological studies shows that the short-term effects of exposure to air pollutants in terms of increased symptoms and use of care are beyond doubt. However, few studies have considered drug consumption among the general population, with most investigations relying on either patient panels or small groups in cross-sectional and, more rarely, longitudinal studies. ObjectivesThe objective of this study was to investigate the association between increased concentrations of the principal atmospheric pollutants in continental France and the consumption of inhaled short-acting beta-2 mimetics for asthma, and of nasal antihistamines and corticosteroids for other allergies. MethodsThis study focused on 12 large cities over a period of 8 years. The concentrations of the principal atmospheric pollutants (NO2, O3, PM10) were obtained from air-quality monitoring stations while drug use for asthma and allergy was documented by local pharmacies (n=6442). Conventional statistical methods were used to study any correlation. ResultsThere was a marked correlation between changes in the dispensing of reference drugs for asthma and allergies and monthly variations in the concentration of atmospheric pollutants other than ozone. The strongest association concerned NO2. ConclusionsDrug consumption is associated with exposure to air pollution and may be used as a tracer/proxy of overall exposure at the population level.

Analysis of the Temporal and Spatial Variation of PM2.5 in China Based on the LUR Model

The land use regression (LUR) model is one of the most important systematic methods to simulate the temporal and spatial differentiation of the atmospheric pollutant concentration. To explore the adaptability of the LUR model to the simulation of air pollutants at the national scale in China and the temporal and spatial variation characteristics of fine air particulate matter (PM2.5) in China in 2015 and its correlation with different geographical elements, we built a LUR model. The LUR model is based on a geographically weighted algorithm using PM2.5 data acquired from the national control monitoring site in 2015 as the dependent variable and applying factors such as the type of land use, altitude, population, road traffic, and meteorological elements as independent variables. Based on model regression mapping, we obtained the distributions of monthly and annual PM2.5 concentrations nationwide in 2015 and analyzed the temporal and spatial variation characteristics of PM2.5 concentrations using the Hu line as a reference line. The results indicate that introducing the geographically weighted algorithm can significantly reduce the residual Moran's Ⅰ of the LUR model, weaken the spatial autocorrelation of residuals, and improve the coefficient of determination R2, which is better to reveal the complex relationship between the spatial distribution and impact factors of PM2.5. Cropland, forest, grass and urban industrial and residential land, and meteorological elements and major roads noticeably impact the PM2.5 concentration. Different spatial distributions of different geographical elements have distinct effects on PM2.5. The PM2.5 shows distinct temporal and spatial differences on both sides of the Hu line. The PM2.5 concentration is relatively high in developed cities with a large population and high industrialization levels. The concentration of PM2.5 is higher in winter and gradually decreases in autumn, spring, and summer.

Characteristics of Air Pollutant Concentration Change and Its Relationship with Meteorological Conditions in Wuhan 2017

In order to reveal the law of the influence of meteorological changes on air pollutants, the author collected the air pollution situation and meteorological data of Wuhan in 2017.Through the analysis of meteorological data and air pollutant data, the time-varying curves of Wuhan air quality index (AQI) and major air pollutants PM2.5, PM10, SO2, NO2, CO and O3 were obtained. Through correlation analysis, the relationship curves of AQI, air pollutants and temperature, rainfall and wind changes are given. The results showed that AQI, PM2.5 and PM10 showed significant u-shaped changes at the monthly scale, with a low level from June to October and a high level at the beginning and end of each year. SO2, NO2 and CO showed "V" changes on the monthly scale, with the lowest points appearing in June, July and July respectively. O3 concentration change in "Λ" type change appears on scales, the highest point in July. Through air pollution index and meteorological data correlation analysis found that: with the increase of temperature, AQI, PM2.5, SO2, NO2, CO concentration decreases, and elevated O3 concentration, PM10 concentration changes with the temperature has little to do; Changes in wind direction, wind is one of the important factors affecting atmospheric pollutants concentration changes, no wind for a long time, can lead to air pollutants gathering, but the wind changes can scrape pollutants, may also put the pollutant of the nonlocal, caused by imported pollution; Spring and winter rainfall can remove air pollutants and improve air quality.

Development and Application of an Atmospheric Pollutant Monitoring System Based on LoRa-Part I: Design and Reliability Tests.

At present, as growing importance continues to be attached to atmospheric environmental problems, the demand for real-time monitoring of these problems is constantly increasing. This article describes the development and application of an embedded system for monitoring of atmospheric pollutant concentrations based on LoRa (Long Range) wireless communication technology, which is widely used in the Internet of Things (IoT). The proposed system is realized using a combination of software and hardware and is designed using the concept of modularization. Separation of each function into independent modules allows the system to be developed more quickly and to be applied more stably. In addition, by combining the requirements of the remote atmospheric pollutant concentration monitoring platform with the specific requirements for the intended application environment, the system demonstrates its significance for practical applications. In addition, the actual application data also verifies the sound application prospects of the proposed system.

Impacts of different types and intensities of El Niño events on winter aerosols over China

El Niño is a strong signal of interannual climate variability. The occurrence of an El Niño event may have an important impact on atmospheric pollutant concentrations in China through changes to the regional climate. This study quantitatively examines the differences among the effects of different types and intensities of El Niño events on winter mean aerosol concentration and severe haze days in China. It is found that Central Pacific (CP) El Niño events lead to larger increases in the aerosol burden over southern China relative to the same intensity of Eastern Pacific (EP) events. For El Niño events of the same type but with different intensities, moderate events induce large increases in surface aerosol concentrations over the entire eastern China, while strong and weak events lead to obvious decreases in the surface aerosol concentration over northern China. These differences are mainly attributed to changes in atmospheric circulation, with the resulting changes in aerosol mass transport occurring in response to the different types of El Niño event. Except for moderate CP events, all kinds of El Niño events lead to an increase in severe haze days in winter over northern China. Strong and weak EP El Niño events and weak CP El Niño events lead to an increase in severe haze days in winter over southern China. A change in the south-north transport of aerosols caused by El Niño events also play a dominate role in changing the occurrence of winter haze days in China.

Ambient fine particulate pollution and daily morbidity of stroke in Chengdu, China.

IntroductionAssociation has been reported between ambient fine particulate matter (PM) and adverse outcomes of cerebrovascular events. However, it remains unclear that whether short-term exposure to PM relates to stroke and the lag of health effects. This triggers us to examine the relationship between PM and population stroke morbidity in Chengdu.MethodsThe daily average concentration of atmospheric pollutants and meteorological factors and daily morbidity of stroke in Chengdu (2013–2015) were collected. Based on time series analysis-generalized additive models (GAM), single-pollutant, two-pollutant and multi-pollutant model were established. The effects of atmospheric PM2.5 (defined as PM less than 2.5μm in aerodynamic diameter), PMc(defined as PM less than 10μm and more than 2.5μm in aerodynamic diameter) and PM10 (defined as PM less than 10μm in aerodynamic diameter) concentration on the daily mortality of stroke were analyzed, respectively.ResultsThe three-year mean concentrations of PM2.5, PMc and PM10 for air pollutants were 75.9, 43.9 and 119.7 μg/m3, respectively. PM2.5 on the current day (lag0) and with a moving average of 0–1 days were significantly associated with the increasing risk of stroke morbidity, and PM2.5 with a lag of 0–1 days had greater association, whereas for PMc and PM10 there were no significant association observed. In our study, every 10μg/m3 increase of PM2.5 was associated with 0.69% percent change in stroke morbidity (95%CI: 0.01~1.38). For females, every 10μg/m3 increase of PM2.5 contributes to 0.80% percent change of onset. And for the group of age less than 65, we observed 0.78% higher risk every 10μg/m3 increase of PM2.5.ConclusionsThese findings suggest that short-term exposure to PM2.5 within 1 day is associated with the onset of stroke, and the younger people (age<65) and females are more sensitive than older people and males.

ChAMBRe: a new atmospheric simulation chamber for aerosol modelling and bio-aerosol research

Abstract. Atmospheric simulation chambers are exploratory platforms used to study various atmospheric processes at realistic but controlled conditions. We describe here a new facility specifically designed for the research on atmospheric bio-aerosol as well as the protocols to produce, inject, expose and collect bio-aerosols. ChAMBRe (Chamber for Aerosol Modelling and Bio-aerosol Research) is installed at the Physics Department of the University of Genoa, Italy, and it is a node of the EUROCHAMP-2020 consortium. The chamber is made of stainless steel with a total volume of about 2.2 m3. The lifetime of aerosol particle with dimension from a few hundreds of nanometres to a few microns varies from about 2 to 10 h. Characteristic parts of the facility are the equipment and the procedures to grow, inject, and extract bacterial strains in the chamber volume while preserving their viability. Bacteria are part of the atmospheric ecosystem and have impact on several levels as: health related issues, cloud formation, and geochemistry. ChAMBRe will host experiments to study the bacterial viability vs. the air quality level, i.e. the atmospheric concentration of gaseous and aerosol pollutants. In this article, we report the results of the characterization tests as well as of the first experiments performed on two bacterial strains belonging to the Gram-positive and Gram-negative groups. A reproducibility at the 10 % level has been obtained in repeated injections and collection runs with a clean atmosphere, assessing this way the chamber sensitivity for systematic studies on bacterial viability vs. environmental conditions.

Links between recent trends in airborne pollen concentration, meteorological parameters and air pollutants

Biogenic aerosols may play an active role in various diseases. Pollutant gases and bioaerosols coexist in the atmosphere with the possibility of interacting with each other increasing their adverse impacts on human health. The study of long-term trends and the correlation between the pollen concentration from selected taxa (especially those related to allergies) and both the main atmospheric pollutants and the meteorological parameters has enabled us to identify the main factors that affect pollen concentration in the atmosphere. This study analyzes the long-term trend in CO, NO, NO2, PM10, SO2 and O3 from 1997 to 2016 and Fraxinus, Poaceae and Populus pollen concentrations in the city of León from 1994 to 2016. In general, there is a significant decreasing trend in atmospheric pollutant concentrations and a significant increasing trend in Fraxinus pollen concentrations. In addition, the influence of air pollutants and climatic factors on pollen concentrations and pollination period duration was studied using the Spearman correlation, showing that the flowering and pollination periods depend largely of the weather conditions before these periods and are influenced by air pollutant concentrations.

Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization

In order to improve the forecasting accuracy of atmospheric pollutant concentration, a prediction model of atmospheric PM2.5 and nitrogen dioxide (NO2) concentration based on support vector regression (SVR) is established. Quantum-behaved particle swarm optimization (QPSO) algorithm is used to select the optimal parameters influencing the performance of SVR. And in order to improve the problem that the fixed SVR model is difficult to adapt to the highly nonlinear process, a simple online SVR based on re-modeling method is proposed instead of the fixed one. According to hourly PM2.5 and NO2 concentrations and meteorological conditions from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the experiment is carried out based on the data of 3 months. Meanwhile, PM2.5 concentration is predicted by three different prediction methods, including the recursive prediction method, direct prediction method, and online direct prediction method. The results show that the online direct prediction method is the most accurate in the three prediction methods. In addition, compared with original particle swarm optimization (PSO) algorithm, QPSO algorithm is tested more efficiently for the improvement of global search ability and robustness during the procedure of parameter selection. Moreover, the hybrid QPSO-SVR model proposed in this paper has higher prediction accuracy and less computational time compared with the PSO-SVR model, genetic algorithm (GA)-SVR model, and grid search (GS)-SVR model, which indicates reliability and effectiveness of the QPSO-SVR model in prediction of these two pollutant concentrations.

Natural and anthropogenic contributions to long-term variations of SO2, NO2, CO, and AOD over East China

Concentrations of atmospheric pollutants over East China have varied considerably during the past decades. These variations are partly due to variations of human activities, e.g., increasing energy consumption and implementation of government emission control policies, and partly to natural fluctuations. This study aims to separate the effects of natural processes and anthropogenic activities on the increase/decrease of the concentrations of some of the most important pollutants (SO2, NO2, CO and aerosols) over East China in the last decade. This was achieved by the comparison of the temporal variations in long-term time series of satellite-retrieved aerosol optical depth (AOD) and vertical column densities (VCDs) of SO2, NO2, and CO, with those in model-simulated time series of the natural variations only. The latter were created by the use of the same anthropogenic emissions throughout the whole simulation, while using re-analysis data (MERRA) to describe meteorological processes and natural emissions. Thus, the comparison between observed and simulated temporal variations reveals the effects due to anthropogenic emissions only, assuming that the atmospheric processes affect natural and anthropogenic species in the same way. In the analysis, a Kolmogorov–Zurbenko (KZ) filter is used to extract long-term components from both the observed and simulated data and normalization to the situation at a certain reference point is used to eliminate bias between observations and simulations. By this new method, natural and anthropogenic contributions to long-term variations of trace gases and AOD are quantitatively estimated. The results show that NO2 VCDs increased from 2004 to 2011 by 76% and of the overall increase in this period only 1% ± 1% was attributed to natural factors, 99% ± 1% attributed to anthropogenic factors. AOD increased by 24% between 2001 and 2011 and of the overall increase 24% ± 32% was due to natural factors and 76% ± 32% was due to anthropogenic factors. SO2 VCDs decreased by 15% from 2007 to 2013, natural and anthropogenic factors contributed respectively 16% ± 14% and 84% ± 14% to the overall decrease in this period. CO decreased since 2003 with 13% and of the overall decrease 6% ± 6% was due to natural factors and 94% ± 6% was due to anthropogenic factors.

Effects of climate change on the health of citizens modelling urban weather and air pollution

A dynamical downscaling tool has been implemented to understand the impacts of global climate on citizens health. We have used the WRF-Chem mesoscale model (NOAA, USA) to produce information covering Europe with 25 km of spatial resolution and two nested domains with 5 km and 1 km of spatial resolution over London. Finally, detailed simulations are carried out using the MICROSYS-CFD model to take into account the effects of urban buildings on the urban atmosphere in the Kensington and Chelsea area. The tool produces very high spatial air quality and meteorological data (50 m) and also temporal resolution (1 h) to estimate health impacts in the short term, using exposure-response functions extracted from epidemiological studies. The comparison shows an acceptable agreement of the modelled data with the measurements. The effects on the health of citizens by temperature change in the future are more important than by changes in atmospheric pollutant concentrations. The maps show how the effects depend on the city's geometry and how the tool can highlight the most vulnerable areas to help to design plans and implement strategic measures to mitigate the effects of global climate change on people's health.

Roadside atmospheric pollution: still a serious environmental problem in Beijing, China

China has made great efforts to reduce ambient atmospheric pollutant concentrations in the past few decades. The air quality in northern China has improved greatly. However, most research has focused on atmospheric pollution in non-roadside environments that have little influence from traffic flow and are impacted by less vehicle exhaust. In this study, hourly air quality monitoring data were collected at four traffic stations and four nontraffic stations in Beijing from June 2014 to September 2017 to analyze the spatial-temporal variations of atmospheric pollutants related to traffic. The traffic stations had higher concentrations of PM2.5, PM10, SO2, NO2, and CO but lower concentrations of O3. Although the overall air quality in Beijing is improving, the pollution rates of PM2.5, PM10, and NO2 in the traffic stations were still high at 47.91%, 50.71%, and 61.22% concentrations, respectively. The air pollution levels in traffic environments are systematically higher than those in nontraffic environments, during both daytime and nighttime, except O3. Furthermore, the traffic stations near ring roads with large numbers of diesel trucks were even more polluted, suggesting the influence of traffic emissions. Under adverse meteorological dispersion conditions, both the traffic and nontraffic stations had high pollution levels, but the pollution at the traffic stations was much higher. To reduce the air pollution level, a series of vehicle restriction rules have been imposed, including license plate restriction. Our analysis revealed obvious cycles associated with license plate restriction rules, suggesting the possibility for further improvement in vehicle restriction rules. The results from our study suggest that roadside environments with heavy traffic in Beijing, China, are still highly polluted and need further efforts to improve.

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China.

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of -0.0043 ppb, mean absolute error of 0.4836 ppb, root mean squared error of 0.5320 ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.

Estimation of Power Plant Emissions With Unscented Kalman Filter

Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO $_X$ ) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a “bottom-up” approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO $_2$ ) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO $_2$ emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas.

ATMOSPHERIC CONCENTRATIONS OF INORGANIC POLLUTANTS (NO2, SO2 AND OZONE) IN ESKİŞEHİR: SPATIAL AND VERTICAL VARIATIONS, WEEKDAY-WEEKEND DIFFERENCES

Atmospheric concentrations of inorganic pollutants (NO 2 , SO 2 and ozone) were measured at three different sampling sites in Eskisehir. Samples were collected between 25 th of March and 8 th of April 2016 by using passive sampling method. During two-week sampling study, each week was divided to three sub-periods to investigate weekday and weekend differences of the pollutant concentrations. One-time v ehicle counts during morning, noon and evening hours for both weekday and weekend periods were inspected. The highest NO 2 concentrations were measured at the sampling site having urban, traffic and residential characteristics. Positive relationship between NO 2 concentrations and traffic density was observed. Reverse trend was determined for ozone. Ozone concentrations were higher at the sampling site far from the city center. Spatial variations of SO 2 concentrations were similar to those of NO 2 . Weekday NO 2 concentrations were higher than weekend concentrations while higher ozone concentrations were observed in weekend. Weekend and weekday SO 2 concentrations were quite similar at all sampling sites. Beside spatial variations, vertical variations of each pollutant were also investigated. NO 2 concentrations decreased with the elevation and reverse case was observed for ozone with increasing concentrations at the same elevation range. SO 2 concentrations increased with the elevation due to chimney gas emissions.