Air Pollution Monitoring Stations Research Articles

Optimization of air pollutant monitoring stations with constraints using genetic algorithm

The design of air quality monitoring stations (AQMS) is very important for environmental protection department. The task, however, has been proven to be in the class of nondeterministic polynomial (NP)-hard problem. The powerful search capability of

Evaluation of MARS for the spatial distribution modeling of carbon monoxide in an urban area

Spatial distribution modeling of CO in Tehran can lead to better air pollution management and control, and it is also suitable for exposure assessment and epidemiological studies. In this study MARS (Multi–variate Adaptive Regression Splines) is compared with typical interpolation techniques for spatial distribution modeling of hourly and daily CO concentrations in Tehran, Iran. The measured CO data in 2008 by 16 monitoring stations were used in this study. The Generalized Cross Validation (GCV) and Cross Validation techniques were utilized for the parameter optimization in the MARS and other techniques, respectively. Then the optimized techniques were compared based on the mean absolute of percentage error (MAPE). Although the Cokriging technique presented less MAPE than the Inverse Distance Weighting, Thin Plate Smooth Splines and Kriging techniques, MARS exhibited the least MAPE. In addition, the MARS modeling procedure is easy. Therefore, MARS has merit to be introduced as an appropriate method for spatial distribution modeling. The number of air pollution monitoring stations is very low (16 stations for 22 zones) and the distribution of stations is not suitable for spatial estimation, hence the level of errors was relatively high (more than 60%). Consequently, hourly and daily mapping of CO provides a limited picture of spatial patterns of CO in Tehran, but it is suitable for estimation of relative CO levels in different zones of Tehran. Hence, the map of mean annual CO concentration was generated by averaging daily CO distributions in 2008. It showed that the most polluted regions in Tehran are the central, eastern and southeastern parts, and mean annual CO concentration in these parts (zones 6, 12, 13,14 and 15) is between 4.2 and 4.6 ppm.

A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas

Parameter and structure identifications are necessary in any modelling which aims to achieve a generalised model. Although ANFIS (Adaptive Network-based Fuzzy Inference System) employs well-known parameter-identification techniques, it needs to structure identification techniques for the determination of an optimum number of fuzzy rules and the selection of significant input variables from among the candidate input variables. In this study, a new structure identification scheme is developed and introduced, which is simultaneously capable of the selection of significant input variables and the determination of an optimum number of rules. This new structure identification was joined to ANFIS, and this joined modelling framework was applied to the simulation of virtual air-pollution monitoring stations in Berlin. In this study, 18 virtual particulate matter stations were simulated using the particulate matter data of some of the current stations. In other words, the particulate matter monitoring network of Berlin has been intensified. The evaluation of simulated virtual stations shows that, although the uncertainty of daily particulate matter measurement is about 10 percent, the simulated virtual stations can estimate the mean daily particulate matter with less than 10 percent of error. Mean absolute error and root mean square error of the simulations are less than 2.4 and 3.4µg/m3, respectively. The correlation coefficient of the simulation results was more than 0.94. In addition, the range of mean bias error is between −1.0 and 0.5µg/m3, and the range of factor of exceedance is between −14.8 and 10.8 percent. It means that the simulated virtual stations have a small bias. These results demonstrated the appropriate performance of the joined new structure identification scheme and ANFIS for development of a virtual air pollution monitoring network.

Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of “dirty” air and “clean” air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries’ air quality forecasting systems.

New Technique for Ranking of Air Pollution Monitoring Stations in the Urban Areas Based upon Spatial Representativity (Case Study: PM Monitoring Stations in Berlin)

The spatial representativity of monitoring stations plays a major role for the reasonable estimation of air pollutants. The ranking of air pollution monitoring stations based upon their spatial representativity identifies the level of representativeness of the stations and is very useful for developing optimum monitoring networks. In this study, a new ranking method, named RTFI (Ranking Technique based upon Fuzzy Interpolation) is introduced. This ranking method is able to rank air pollution monitoring stations in the urban areas based upon their spatial representativity. Although spatial correlation techniques are often used in the ranking techniques in order to consider spatial representativity, in this ranking technique, the spatial representativity of a station is not limited to its surroundings and is measured independently of its location. RTFI was applied to airborne Particulate Matter (PM) at seven stations in Berlin, and ranked them according to their spatial representativity. The results showed that the Neukolln-Nanenstr station (MC 42) is the most spatially representative station among the studied stations.

Particulate Matters Pollution Characteristic and the Correlation between PM (PM<sub>2.5</sub>, PM<sub>10</sub>) and Meteorological Factors during the Summer in Shijiazhuang

In recent years, the haze occurs frequently and air pollution is getting worse in Beijing-Tianjin-Hebei Region, China. The particulate matter pollution characteristic researches are playing a sig-nificant role especially in the districts where have higher concentration PM and air pollution. In this study, we collected daily particulate matter (PM10, PM2.5) mass concentration data from 7 air pollution monitoring stations in Shijiazhuang City, Hebei, China over a 3-month period from June to August to investigate particulate matter pollution characteristic and the relationship with me-teorological conditions. Statistical results show that PM10 is the major pollutant in Shijiazhuang City; the average daily concentrations of PM2.5 and PM10 are 94.45 μg/m3 and 219.15 μg/m3, respectively. The daily average of PM10 and PM2.5 level over the period exceeded the first grade of the daily average limit of the ambient air quality standards (GB3095-2012). And there is a significantly positive correlation between atmospheric pressure and particulate matter pollution, but there is a significantly negative correlation between atmospheric temperature and PM concentrations. Precipitation has a clear role mainly in the coarse particles; however, there has little effect on fine particulate matter. Relative humidity and wind speed have a poor correlation with atmospheric pollutant concentrations (not remarkably high).

Comparison between different dynamical downscaling methods using WRF-Chem for urban applications: Madrid case study

The European Centre for Medium-Range Weather Forecasts is used to provide boundary conditions for the mesoscale model WRF-Chem that has been run over Europe with 23 km spatial resolution. We have used a full one-way nesting approach to produce simulations centred over the city of Madrid (Spain) with 4.6 km spatial resolution, 0.92 km spatial resolution and 0.184 km spatial resolution. In last level, we have run the CMAQ (full chemistry) model to produce chemical pollution data. This is called the control reference simulation. The simplified and faster downscaling procedure used in this experiment is the CALMET-CMAQL (linear chemistry) model. Both downscaling techniques are compared using meteorological and air pollution monitoring station. The comparison between both downscaling techniques shows that the CALMET-CMAQL model is much faster and computationally cheap; the results are good enough to consider this tool for climate purposes.

Estimation of ozone concentrations above forests using atmospheric observations at urban air pollution monitoring stations

Estimation of ozone concentrations above forests using atmospheric observations at urban air pollution monitoring stations

Relation of particle matter on roadside tree leaves with traffic density and road dust

In Japan, air pollution due to atmospheric particulate matter has been a serious problem, especially at roadsides in megacity areas. However, it is difficult to widely cover roadside areas with a limited number of air pollution monitoring stations. Therefore we focused on a biomonitoring technique by using leaves of roadside trees. We sampled leaves of ginkgo (G. biloba) along major arteria roads in Osaka City, which is the third largest city in Japan. The particles retained on the surfaces of the sampled leaves were removed by ultrasonic cleaning in ethanol solution. The ethanol solution including particles was suctioned by an aspirator and particles were collected on a PTFE filter. Elements collected on the filter were quantified by particle induced X-ray emission (PIXE) analysis. It was found that the mass of traffic-related elements on the surfaces of sampled leaves correlated with the traffic density of large vehicles and the distance from the end of roadside to the sampling point. The ratios of mass of road dust-related elements on sampled leaves were almost similar to those of the road dust profile data. These results suggest the biomonitoring technique is an effective method to assess roadside air pollution.

Radiocarbon analysis of elemental and organic carbon in Switzerland during winter-smog episodes from 2008 to 2012 – Part 1: Source apportionment and spatial variability

Abstract. While several studies have investigated winter-time air pollution with a wide range of concentration levels, hardly any results are available for longer time periods covering several winter-smog episodes at various locations; e.g., often only a few weeks from a single winter are investigated. Here, we present source apportionment results of winter-smog episodes from 16 air pollution monitoring stations across Switzerland from five consecutive winters. Radiocarbon (14C) analyses of the elemental (EC) and organic (OC) carbon fractions, as well as levoglucosan, major water-soluble ionic species and gas-phase pollutant measurements were used to characterize the different sources of PM10. The most important contributions to PM10 during winter-smog episodes in Switzerland were on average the secondary inorganic constituents (sum of nitrate, sulfate and ammonium = 41 ± 15%) followed by organic matter (OM) (34 ± 13%) and EC (5 ± 2%). The non-fossil fractions of OC (fNF,OC) ranged on average from 69 to 85 and 80 to 95% for stations north and south of the Alps, respectively, showing that traffic contributes on average only up to ~ 30% to OC. The non-fossil fraction of EC (fNF,EC), entirely attributable to primary wood burning, was on average 42 ± 13 and 49 ± 15% for north and south of the Alps, respectively. While a high correlation was observed between fossil EC and nitrogen oxides, both primarily emitted by traffic, these species did not significantly correlate with fossil OC (OCF), which seems to suggest that a considerable amount of OCF is secondary, from fossil precursors. Elevated fNF,EC and fNF,OC values and the high correlation of the latter with other wood burning markers, including levoglucosan and water soluble potassium (K+) indicate that residential wood burning is the major source of carbonaceous aerosols during winter-smog episodes in Switzerland. The inspection of the non-fossil OC and EC levels and the relation with levoglucosan and water-soluble K+ shows different ratios for stations north and south of the Alps (most likely because of differences in burning technologies) for these two regions in Switzerland.

A novel hybrid forecasting model for PM10 and SO2 daily concentrations

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM10 (particles with a diameter of 10μm or less) concentrations and SO2 (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising.

INVESTIGATING THE RELATION BETWEEN PREVALENCE OF ASTHMATIC ALLERGY WITH THE CHARACTERISTICS OF THE ENVIRONMENT USING ASSOCIATION RULE MINING

Abstract. The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus and, then, avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this paper, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that.

First retrieval of hourly atmospheric radionuclides just after the Fukushima accident by analyzing filter-tapes of operational air pollution monitoring stations.

No observed data have been found in the Fukushima Prefecture (FP) for the time-series of atmospheric radionuclides concentrations just after the Fukushima Daiichi Nuclear Power Plant (FD1NPP) accident. Accordingly, current estimates of internal radiation doses from inhalation, and atmospheric radionuclide concentrations by atmospheric transport models are highly uncertain. Here, we present a new method for retrieving the hourly atmospheric 137Cs concentrations by measuring the radioactivity of suspended particulate matter (SPM) collected on filter tapes in SPM monitors which were operated even after the accident. This new dataset focused on the period of March 12–23, 2011 just after the accident, when massive radioactive materials were released from the FD1NPP to the atmosphere. Overall, 40 sites of the more than 400 sites in the air quality monitoring stations in eastern Japan were studied. For the first time, we show the spatio-temporal variation of atmospheric 137Cs concentrations in the FP and the Tokyo Metropolitan Area (TMA) located more than 170 km southwest of the FD1NPP. The comprehensive dataset revealed how the polluted air masses were transported to the FP and TMA, and can be used to re-evaluate internal exposure, time-series radionuclides release rates, and atmospheric transport models.

Impact of short-term exposure to fine particulate matter on emergency ambulance dispatches in Japan

BackgroundEvidence of an association between fine particulate matter (PM2.5) and morbidity is limited in Asia. We used a case-crossover design to evaluate the association between short-term exposure to PM2.5 and...

Field evaluation of a near–real time elemental monitor and identification of element sources observed at an air monitoring supersite in Korea

Ambient measurements of elemental species concentrations were made using an online elemental monitor at an air pollution monitoring station in Gwangju, Korea to evaluate the performance of the monitor for near–real time PM2.5 elemental monitoring and identify possible sources of the observed elements. This study also demonstrates the utility of integrating hourly elemental data with the meteorological data to better understand the sources of elements. Good agreement between the online and filter–based measurements was obtained for the elements studied, with an R2 of 0.73–0.97 and regression slopes of 0.84–2.37, suggesting the potential use of the online monitor to observe temporal variations in anthropogenic aerosol particles. Atmospheric element concentration levels studied were 2–20 times greater than their background levels when pollution plumes coming from industrial areas had impacted the site. Examination of the elements data revealed several short spikes of elements that are associated with local industrial emissions and road dusts. For the haze episodes observed over the study period, the element K was significantly influenced by biomass burning emissions with some impacts from soil dusts and industrial activities. Based on the wind direction and wind speed data, As/Se, Se/SO42−, and correlations among Se, As, Pb, SO2, and SO42−, it is suggested that the observed As and Se were significantly impacted by local anthropogenic sources and long–range transport of aerosols, rather than local coal combustion or coke emissions. Conditional probability functions were applied to identify likely local emission source locations of the elements observed at the site, indicating that the elements contributions were from the southwest and northeast directions, where two industrial areas and an express highway are located. Results from principal component analysis indicate that the observed concentrations of the element species were likely attributed to road dust/local industrial emissions, oil combustion, and metal processing.

Effects of ambient air pollution on the prevalence of pneumonia in children: Implication for National Ambient Air Quality Standards in China

Pneumonia is the most important cause of child death in the world today. Recent findings suggest that exposure to outdoor air pollutants may increase the pneumonia occurrence, but so far the associations have not been clearly illustrated. Therefore, the effects of outdoor air pollutants and the episode day on childhood pneumonia were investigated by this study. A standardized questionnaire on health, home and environmental factors of 4988 children was conducted in the city of Changsha, China. Average exposure to ambient air pollutants during 2008–2011 was monitored by the air pollution monitoring stations of Changsha Environmental Protection Agency. A two-stage hierarchical regression approach was adopted to estimate the effects of PM10, SO2 and NO2 on pneumonia risk. The prevalence of pneumonia in children aged 3–6 years was 38.2%, which is greatly higher than the developed countries but lower than some developing countries. Furthermore, the increased risk of childhood pneumonia was significantly associated with elevated NO2 concentration (odds ratio (OR) = 1.157, 95% confidence interval (CI): 1.121–1.195) and the episode day (OR = 1.038, 95% CI: 1.024–1.051), but not significantly associated with PM10 and SO2. Exposure to traffic-related air pollution could play an important role in the development of pneumonia in preschool children. The revised National Ambient Air Quality Standards (2012) for NO2 has provided a better control strategy for ambient air quality to reduce the risk of adverse health effects such as pneumonia for children.

Predicting seasonal and spatial patterns of long-term nitrogen oxides concentration in Tehran, Iran using land use regression

Background: Tehran, the capital city of Iran in the Middle East, experiences extreme air pollution concentrations. Aims: Long-term spatial and seasonal patterns of nitrogen oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations were estimated by land use regression (LUR). Methods: Hourly measurements of NO, NO2 and NOx were obtained from 23 automatic air pollution monitoring stations spread across the metropolitan area of Tehran. In addition, 210 variables were compiled using a Geographic Information System. Finally, annual and seasonal models (cooler and warmer season) were built using multiple linear regression with a novel step-by-step algorithm. Results: The annual mean concentrations of NO, NO2 and NOx were 88.1, 53.1, and 141.8 ppb, respectively. The cooler season mean concentrations were 117, 20, and 180.2 ppb, respectively and the warmer season mean concentrations were 60, 44.6, and 104.7 ppb, respectively. The leave-one-out cross-validation (LOOCV) R2 values for the LUR models ranged from 0.50 to 0.84 for NO, from 0.59 to 0.69 for NO2, and from 0.70 to 0.77 for NOx. The most predictive variables for NO included measures of distance to traffic, while those for NO2 and NOx were more influenced by industrial sources. Conclusions: Resulting models and maps show that patterns were consistent for the annual and cooler season models for NO, NO2 and NOx, but there were clear differences for warmer seasons.

EVALUASI TINGKAT POLUTAN DARI KEGIATAN PENGELOLAAN SAMPAH DI RUMAH KOMPOS SRIKANA DAN KEPUTRAN KOTA SURABAYA

Garbage materials are able to contaminate the environment in three ways:physically, chemically, and biologically. The large composition of garbage require managementand processing to reduce garbage load entering the TPA. In relation to this, Dinas Kebersihandan Pertamanan Kota 5urabaya has initiated composting of organic wastes partly to reducetheir volume. This study was aimed at measuring pollutants in compost materials especially interms of CO, 502, and H25 parameters. This activity was carried out in Composting Houses of5rikana and Keputran 5urabaya.This descriptive study was performed by measuring CO, SOb and H25 andlaboratory examination. The population under study was composting houses (18 sites) and thesample size was 2 composting houses.Results showed that pollutant level of CO, 502 and H25 in the air was not exceedingthe quality standard being implemented in East Java. The CO level in 5rikana CompostingHouse was 4.21 «(JgjNm3) and in residential area was 3.37 «(JgjNm3). 502 level in compostinghouse was 12.04 «(Jg/Nm3), while outside of house of 16.71 «(JgjNm3) and in residential areawas 4.16 «(Jg/Nm3). The H25 level in composting house was 9.13 «(Jg/Nm3), outside the housewas 6.54«(JgjNm3) and in residential area was 3.22 «(JgjNm3). The CO level in KeputranComposting House was 9.66 «(Jg/Nm3), in outside of the house was 15,74 (uq/Nrn") and in thesurrounding market was 4.18 «(Jg/Nm3). The 502 level in composting house was 6,88«(Jg/Nm3), outside of compost house 19.38«(Jg/Nm3), and in the surrounding market was 2,76«(Jg/Nm3). H2S level in composting house was 16,14 «(Jg/Nm3), in outside of the house was4,49 «(JgjNm3) and in the surrounding market was 1.97 «(Jg/Nm3). Temperature, humidity, andwind velocity lase have some influence as well but not too significant to increase pollutantlevel.It is suggested to Dinas Kebersihan dan Pertamanan Kota Surabaya to addcomposting house in its list of air pollution monitoring station.

Local and regional sources of fine and coarse particulate matter based on traffic and background monitoring

The aim of this study was to identify local and exogenous sources affecting particulate matter (PM) levels in five major cities of Northern Europe namely: London, Paris, Hamburg, Copenhagen and Stockholm. Besides local emissions, PM profile at urban and suburban areas of the European Union (EU) is also influenced by regional PM sources due to atmospheric transport, thus geographical city distribution is of a great importance. At each city, PM10, PM2.5, NO2, SO2, CO and O3 air pollution data from two air pollution monitoring stations of the EU network were used. Different background characteristics of the selected two sampling sites at each city facilitated comparisons, providing a more exact analysis of PM sources. Four source apportionment methods: Pearson correlations among the levels of particulates and gaseous pollutants, characterisation of primal component analysis components, long-range transport analysis and extrapolation of PM size distribution ratios were applied. In general, fine (PM2.5) and coarse (PM10) particles were highly correlated, thus common sources are suggested. Combustion-originated gaseous pollutants (CO, NO2, SO2) were strongly associated to PM10 and PM2.5, primarily at areas severely affected by traffic. On the contrary, at background stations neighbouring important natural sources of particles or situated in suburban areas with rural background, natural emissions of aerosols were indicated. Series of daily PM2.5/PM10 ratios showed that minimum fraction values were detected during warm periods, due to higher volumes of airborne biogenic PM coarse, mainly at stations with important natural sources of particles in their vicinity. Hybrid single-particle Lagrangian integrated trajectory model was used, in order to extract 4-day backward air mass trajectories that arrived in the five cities which are under study during days with recorded PM10 exceedances. At all five cities, a significantly large fraction of those trajectories were classified in short- and medium-range clusters, thus transportation of particulates along with slow moving air masses was identified. A finding that supports the assumption of long-range transport is that, at background stations, long-range transportation effects were stronger, in comparison to traffic stations, due to less local particle emissions. Short-range trajectories associated to PM transport in Stockholm, Copenhagen and Hamburg were mainly of a continental origin. All three cities were approached by slow moving air masses originated from Poland and the Czech Republic, whereas Copenhagen and Stockholm were also influenced by short-range trajectories from Germany and France and from Jutland Peninsula and Scandinavian Peninsula, respectively. London and Paris are located to the north-west part of Europe. Trajectories of short and medium length arrived to these two megacities mainly through France, Germany, UK and North Atlantic.

Spatial and temporal characteristics of particulate matter in Beijing, China using the Empirical Mode Decomposition method

Air pollution has become a serious problem in Beijing, China. Daily PM10 mass concentration measurements were collected at 27 stations in Beijing over a 5-year period from January 1, 2008 to October 31, 2012. We used a new clustering method (kernel K-means) and a new period and trend decomposition method (Empirical Mode Decomposition, EMD) to explore the spatial and temporal characteristics of the PM10 mass concentration in the City. The temporal period and trend of each cluster center were decomposed using the EMD method, which is an adaptive data analysis method that requires no prior information. The daily PM10 mass concentrations varied greatly from 5μg/m3 to more than 600μg/m3. All of the stations were partitioned into three clusters by the kernel K-means method, and which represent the low-, middle- and high-pollution stations, respectively. The first cluster contained nine stations, mainly located in the north suburban area. The second cluster, whose degree of pollution was much more serious than the first cluster, contained 13 stations distributed in urban and peri-urban areas. The pollution level in the southern part of Beijing was much more serious than in the northern part of the City. The third cluster contained five stations located outside the second-cluster stations. The total decreased amplitudes of the three clusters during the whole period were 19μg/m3, 10μg/m3 and 4μg/m3, respectively. Although the global trend of the PM10 mass concentration decreased in general, it was not the same for each season and station. The trends in summer and winter declined, while in spring, it has been increasing in recent years. Five types of trends can be found for stations, including monotonic decreasing, rise fall, fall rise fall, fall rise and rise. The rising trend of the regional background air pollution monitoring station, Miyun-reservoir, indicates an increase in the City's background PM10 mass concentration.