Assessment of natural components of PM10 at UK urban and rural sites

Proposals from the European Commission have raised the possibility that Member States may be able to subtract the concentrations of natural components of airborne particulate matter from measured concentrations when evaluating compliance with EU Limit Values. By applying the pragmatic mass closure model [Harrison et al., 2003. A pragmatic mass closure model for airborne particulate matter at urban background and roadside sites. Atmospheric Environment 37, 4927–4933] to chemical composition data for PM₁₀, it has been possible to estimate the concentrations of natural sea salt, strongly bound water and secondary organic carbon (which is assumed wholly biogenic) to the measured mass of PM₁₀. Because of the difficulty in distinguishing between natural and anthropogenic crustal dusts, the contribution of natural windblown dust and soil has not been accounted for. When the natural components are estimated for two urban and one rural site in the UK, the long-term mean PM₁₀ concentration is reduced by between 5.2 and 7.3 μg m⁻³. The number of exceedences of the 50 μg m⁻³ 24-h limit value falls dramatically from 54 to 21 (from a total of 291 days) at an urban street canyon site, 7 to 3 (n=292 days) at an urban background site and from 8 to 0 (n=241 days) at a rural site when using gravimetric PM₁₀ concentrations. The calculations have also been performed using PM₁₀ concentrations measured by TEOM increased by a factor of 1.3 as recommended by the European Commission as an interim means of estimating gravimetric equivalency, and the number of exceedences of the 24-h limit value fell from 92 to 47 (from a total of 291 days) at the urban street canyon site, from 11 to 3 (n=292 days) at the urban background site and from 6 to 3 (n=241) at the rural site. Clearly, therefore, application of this proposed measure would make a very major difference to the likelihood of compliance or otherwise with the 24-h limit value for PM₁₀.     

Long-term variation of PM2.5 levels and composition at rural, urban, and roadside sites in Hong Kong: Increasing impact of regional air pollution

Long-term study of air pollution plays a decisive role in formulating and refining pollution control strategies. In this study, two 12-month measurements of PM_2.5 mass and speciation were conducted in 00/01 and 04/05 to determine long-term trend and spatial variations of PM_2.5 mass and chemical composition in Hong Kong. This study covered three sites with different land-use characteristics, namely roadside, urban, and rural environments. The highest annual average PM_2.5 concentration was observed at the roadside site (58.0±2.0 μg m⁻³ (average±2σ) in 00/01 and 53.0±2.7 μg m⁻³ in 04/05), followed by the urban site (33.9±2.5 μg m⁻³ in 00/01 and 39.0±2.0 μg m⁻³ in 04/05), and the rural site (23.7±1.9 μg m⁻³ in 00/01 and 28.4±2.4 μg m⁻³ in 04/05). The lowest PM_2.5 level measured at the rural site was still higher than the United States’ annual average National Ambient Air Quality Standard of 15 μg m⁻³. As expected, seasonal variations of PM_2.5 mass concentration at the three sites were similar: higher in autumn/winter and lower in summer. Comparing PM_2.5 data in 04/05 with those collected in 00/01, a reduction in PM_2.5 mass concentration at the roadside (8.7%) but an increase at the urban (15%) and rural (20%) sites were observed. The reduction of PM_2.5 at the roadside was attributed to the decrease of carbonaceous aerosols (organic carbon and elemental carbon) (>30%), indicating the effective control of motor vehicle emissions over the period. On the other hand, the sulfate concentration at the three sites was consistent regardless of different land-use characteristics in both studies. The lack of spatial variation of sulfate concentrations in PM_2.5 implied its origin of regional contribution. Moreover, over 36% growth in sulfate concentration was found from 00/01 to 04/05, suggesting a significant increase in regional sulfate pollution over the years. More quantitative techniques such as receptor models and chemical transport models are required to assess the temporal variations of source contributions to ambient PM_2.5 mass and chemical speciation in Hong Kong.     

石家庄市道路交通环境NO_x污染水平及ARMA模型预测

选择石家庄市区代表性路段作为研究对象,对其交通环境空气中NOx的污染水平进行现状监测。基于Matlab软件建立拟合模型,对下一时期的NOx污染趋势进行预测。结果表明,石家庄市交通环境中NOx小时浓度介于0.047~0.237 mg/m3之间,呈早晚高,且下午明显低于上午的日变化规律;NOx日均浓度介于0.076~0.211 mg/m3之间,其浓度与车流量呈明显的正相关性。利用matlab软件建立的ARMA模型能够较好地预测道路交通环境空气中NOx的浓度变化趋势。     

The impact of wood smoke on ambient PM2.5 in northern Rocky Mountain valley communities

During the winters of 2006/2007 and 2007/2008, PM_2.5 source apportionment programs were carried out within five western Montana valley communities. Filter samples were analyzed for mass and chemical composition. Information was utilized in a Chemical Mass Balance (CMB) computer model to apportion the sources of PM_2.5. Results showed that wood smoke (likely residential woodstoves) was the major source of PM_2.5 in each of the communities, contributing from 56% to 77% of the measured wintertime PM_2.5. Results of ¹⁴C analyses showed that between 44% and 76% of the measured PM_2.5 came from a new carbon (wood smoke) source, confirming the results of the CMB modeling. In summary, the CMB model results, coupled with the ¹⁴C results, support that wood smoke is the major contributor to the overall PM_2.5 mass in these rural, northern Rocky Mountain airsheds throughout the winter months.     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

The impacts of different kinds of dust events on PM10 pollution in northern China

In this study the frequencies of PM₁₀ (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM₁₀ concentration in the high-frequency period is higher with an extent 0.009–0.066 mg m⁻³ than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM₁₀ concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM₁₀ concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM₁₀ is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM₁₀ concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM₁₀ concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM₁₀ concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM₁₀ pollution concentration is usually caused by floating dust.     

Effect of PM2.5 chemical constituents on atmospheric visibility impairment

PM_2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM_2.5 and its chemical components on the visibility impairment. The PM_2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM_2.5 (the water-soluble ionic species SO₄^2-, NO₃^?, Cl^?, and NH₄⁺, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, b_ext. The visibility was found to be negatively correlated with PM_2.5 and its components. The reconstructed b_ext showed that organic matter was the largest contributor to b_ext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures.

Implications: The chemical constituents of PM_2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Traffic generated non-exhaust particulate emissions from concrete pavement: A mass and particle size study for two-wheelers and small cars

This study aimed to understand the non-exhaust (NE) emission of particles from wear of summer tire and concrete pavement, especially for two wheelers and small cars. A fully enclosed laboratory-scale model was fabricated to simulate road tire interaction with a facility to collect particles in different sizes. A road was cast using the M-45 concrete mixture and the centrifugal casting method. It was observed that emission of large particle non exhaust emission (LPNE) as well as PM₁₀ and PM_2.5 increased with increasing load. The LPNE was 3.5 mg tire⁻¹ km⁻¹ for a two wheeler and 6.4 mg tire⁻¹ km⁻¹ for a small car. The LPNE can lead to water pollution through water run-off from the roads. The contribution of the PM₁₀ and PM_2.5 was smaller compared to the LPNE particles (less than 0.1%). About 32 percent of particle mass of PM₁₀ was present below 1 μm. The number as well as mass size distribution for PM₁₀ was observed to be bi-modal with peaks at 0.3 μm and 4–5 μm. The NE emissions did not show any significant trend with change in tire pressure.     

重庆市春季大气颗粒物浓度的对比监测分析

通过2012年春季在重庆大气超级站进行的PM10和PM2.5手工采样与自动仪器的对比监测,分析了自动监测与手工监测的一致性及造成偏差的原因,并对PM2.5与PM10浓度的比值关系进行了分析。结果表明:MP101M型颗粒物自动监测仪用于监测PM10时系统性误差偏高,仪器初始精密度存在负偏差;用于监测PM2.5时系统性误差在允许范围之内,仪器初始精密度存在较大负偏差;PM10和PM2.5的手工采样和自动仪器监测值之变化趋势具有非常高的一致性;PM2.5与PM10浓度比值范围在56.5%～90.4%,平均比值为(73.8±7.4)%。     

北京市交通道路空气中NO_x的污染现状及时空变化规律研究

基于2008年及2009年分4个季节对北京市3种类型道路(开阔型、交叉路口型和峡谷型)空气中的NOx的现场监测结果,分析了3种类型道路空气中NOx的污染现状和时空变化规律及影响因素。实验结果表明,昼间北京市各类型街道空气中NOx浓度呈早晚浓度高、中午浓度低的变化规律,NOx浓度随季节和车流量变化较明显。交通道路空气中NO占NOx的分担率高,且有较好的相关性,而NO2分担率较低,与NOx相关性较差。     

Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II—particulate matter

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM_2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO₄²⁻) predictions. Large overpredictions of particulate nitrate (NO₃⁻) and ammonium (NH₄⁺) in the fall are likely due to a gross overestimation of seasonal ammonia (NH₃) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM_2.5 predictions with observed PM_2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM_2.5 mass in those seasons. The model biases in PM_2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM_2.5 (PM_Other) are largely to blame for the errors in total PM_2.5 mass predictions, and efforts are underway to identify the cause of these errors.     

Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK

Monitoring data from the UK Automatic Urban and Rural Network are used to investigate the relationships between ambient levels of ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂) as a function of NO_x, for levels ranging from those typical of UK rural sites to those observed at polluted urban kerbside sites. Particular emphasis is placed on establishing how the level of ‘oxidant’, OX (taken to be the sum of O₃ and NO₂) varies with the level of NO_x, and therefore to gain some insight into the atmospheric sources of OX, particularly at polluted urban locations. The analyses indicate that the level of OX at a given location is made up of NO_x-independent and NO_x-dependent contributions. The former is effectively a regional contribution which equates to the regional background O₃ level, whereas the latter is effectively a local contribution which correlates with the level of primary pollution. The local oxidant source has probable contributions from (i) direct NO₂ emissions, (ii) the thermal reaction of NO with O₂ at high NO_x, and (iii) common-source emission of species which promote NO to NO₂ conversion. The final category may include nitrous acid (HONO), which appears to be emitted directly in vehicle exhaust, and is potentially photolysed to generate HO_x radicals on a short timescale throughout the year at southern UK latitudes. The analyses also show that the local oxidant source has significant site-to-site variations, and possible reasons for these variations are discussed. Relationships between OX and NO_x, based on annual mean data, and fitted functions describing the relative contributions to OX made by NO₂ and O₃, are used to define expressions which describe the likely variation of annual mean NO₂ as a function of NO_x at 14 urban and suburban sites, and which can take account of possible changes in the regional background of O₃.     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.     

Response of Fine Particulate Matter to Emission Changes of Oxides of Nitrogen and Anthropogenic Volatile Organic Compounds in the Eastern United States

Abstract

A three-dimensional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions [PMCAMx]) is used to investigate changes in fine particle (PM_2.5) concentrations in response to 50% emissions changes of oxides of nitrogen (NO_x) and anthropogenic volatile organic compounds (VOCs) during July 2001 and January 2002 in the eastern United States. The reduction of NO_x emissions by 50% during the summer results in lower average oxidant levels and lowers PM_2.5 (8% on average), mainly because of reductions of sulfate (9–11%), nitrate (45–58%), and ammonium (7–11%). The organic particulate matter (PM) slightly decreases in rural areas, whereas it increases in cities by a few percent when NO_x is reduced. Reduction of NO_x during winter causes an increase of the oxidant levels and a rather complicated response of the PM components, leading to small net changes. Sulfate increases (8–17%), nitrate decreases (18– 42%), organic PM slightly increases, and ammonium either increases or decreases a little. The reduction of VOC emissions during the summer causes on average a small increase of the oxidant levels and a marginal increase in PM_2.5. This small net change is due to increases in the inorganic components and decreases of the organic ones. Reduction of VOC emissions during winter results in a decrease of the oxidant levels and a 5–10% reduction of PM_2.5 because of reductions in nitrate (4–19%), ammonium (4–10%), organic PM (12–14%), and small reductions in sulfate. Although sulfur dioxide (SO₂) reduction is the single most effective approach for sulfate control, the coupled decrease of SO₂ and NO_x emissions in both seasons is more effective in reducing total PM_2.5 mass than the SO₂ reduction alone.     

PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995

PM_2.5 (particles with aerodynamic diameters less than 2.5 μm) chemical source profiles applicable to speciated emissions inventories and receptor model source apportionment are reported for geological material, motor vehicle exhaust, residential coal (RCC) and wood combustion (RWC), forest fires, geothermal hot springs; and coal-fired power generation units from northwestern Colorado during 1995. Fuels and combustion conditions are similar to those of other communities of the inland western US. Coal-fired power station profiles differed substantially between different units using similar coals, with the major difference being lack of selenium in emissions from the only unit that was equipped with a dry limestone sulfur dioxide (SO₂) scrubber. SO₂ abundances relative to fine particle mass emissions in power plant emissions were seven to nine times higher than hydrogen sulfide (H₂S) abundances from geothermal springs, and one to two orders of magnitude higher than SO₂ abundances in RCC emissions, implying that the SO₂ abundance is an important marker for primary particle contributions of non-aged coal-fired power station contributions. The sum of organic and elemental carbon ranged from 1% to 10% of fine particle mass in coal-fired power plant emissions, from 5% to 10% in geological material, >50% in forest fire emissions, >60% in RWC emissions, and >95% in RCC and vehicle exhaust emissions. Water-soluble potassium (K⁺) was most abundant in vegetative burning profiles. K⁺/K ratios ranged from 0.1 in geological material profiles to 0.9 in vegetative burning emissions, confirming previous observations that soluble potassium is a good marker for vegetative burning.     

Major air pollutants in Bursa,Turkey: their levels,temporal changes,interactions, and sources

Bursa is one of the largest cities of Turkey and it hosts 17 organized industrial zones. Parallel to the increase in population, rapidly growing energy consumption, and increased numbers of transport vehicles have impacts on the air quality of the city. In this study, regularly calibrated automatic samplers were employed to get the levels of air pollution in Bursa. The concentrations of CH₄ and N-CH₄ as well as the major air pollutants including PM₁₀, PM_2.5, NO, NO₂, NO_x, SO₂, CO, and O₃, were determined for 2016 and 2017 calendar years. Their levels were 1641.62?±?718.25, 33.11?±?5.45, 42.10?±?10.09, 26.41?±?9.01, 19.47?±?16.51, 46.73?±?16.56, 66.23?±?32.265, 7.60?±?3.43, 659.397?±?192.73, and 51.92?±?25.63 µg/m³ for 2016, respectively. Except for O₃, seasonal concentrations were higher in winter and autumn for both years. O₃, CO, and SO₂ had never exceeded the limit values specified in the regulations yet PM₁₀, PM_2.5, and NO₂ had violated the limits in some days. The ratios of CO/NO_x, SO₂/NO_x, and PM_2.5/PM₁₀ were examined to characterize the emission sources. Generally, domestic and industrial emissions were dominated in the fall and winter seasons, yet traffic emissions were effective in spring and summer seasons. As a result of the correlation process between O_x and NO_x, it was concluded that the most important source of O_x concentrations in winter was NO_x and O₃ was in summer.