Observed PM10 Concentrations Research Articles

Regional classification of high PM10 concentrations in the Seoul metropolitan and Chungcheongnam-do areas, Republic of Korea

Since the Seoul metropolitan area is a highly developed megacity, many people are often exposed to high concentrations of particulate matter (PM), with mean aerodynamic diameters equal to or less than 10 μm (PM10), in cold seasons. PM10 concentrations can be influenced by a combination of various factors, including meteorological conditions, anthropogenic emissions, atmospheric chemical reactions, transboundary transport, and geographic characteristics. However, the establishment of an efficient air quality management plan remains challenging due to the limited understanding of the regional PM concentration characteristics. Here, the Seoul metropolitan (Seoul, Gyeonggi-do, and Incheon) and Chungcheongnam-do (Chungcheongnam-do, Daejeon, and Sejong) areas were regionally classified to identify the spatiotemporal air quality in areas where megacities and emission sources are mixed. The four representative regions were determined using the K-means clustering method based on the temporal variations in the observed PM10 concentrations. The first cluster consisted of small cities in the southern and eastern parts of Gyeonggi-do and Chungcheongnam-do, respectively, while the second cluster consisted of Incheon, West Gyeonggi-do, and Seoul. In addition, the third and fourth clusters included West Chungcheongnam-do and East Gyeonggi-do, which are adjacent to the Yellow Sea and downstream area of the westerly wind, respectively. The characteristics of each cluster during the high PM10 concentration events are explainable by wind patterns and the local air pollutant emissions, including nitrogen oxides and sulfur oxides. The obtained regional classification was different from the provincial-level administrative division of South Korea. Therefore, the present study is expected to be a scientific basis for overcoming the limitations of air quality management in administrative districts.

Health Impact Assessment of Short-Term Exposure to Particulate Matter (PM10) in Northern Thailand.

In northern Thailand, in recent decades, particulate pollution from the burning of biomass has become a serious issue with toxicological implications for human health, especially during the winter months of January to April. The purpose of this study was to explore short-term exposure to particulate matter (PM10) in northern Thailand. The high PM10 concentration in 2012 was used as a case study. We used the EPA's Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) for the health impact assessment, along with ground-based measurement data. The annual average observed PM10 concentration was in the range of 43-61 μg/m3, with a maximum observed PM10 concentration of 300 μg/m3 in March. We then assessed the impacts of PM10 exposure in northern Thailand. When the PM10 concentration was reduced to 120 μg/m3, the undesirable effects on respiratory mortality decreased by 5%-11%. When the concentration of PM10 was reduced to 45 μg/m3, the deleterious effects on respiratory mortality decreased by 11-30%. In conclusion, adherence to the WHO-AQG, particularly for PM10 (45 μg/m3), tends to result in considerable reductions in respiratory disease mortality in northern Thailand.

Air quality assessment for Kunooz Gabbro quarry in Oman: assessment of PM10

This study provided an air quality assessment for Kunooz Gabbro Quarry in Al Jaffnain, Al-Seeb, Oman and focused mainly on emitted particulate matter 10 micrometers or less in diameter (PM10) from different sources inside the plant. Dispersion of PM10 was modeled using CALPUFF software. PM10 particle emissions were modeled for the winter and the summer season. The results of the simulation showed that during the winter season, the maximum one-hour average PM10 concentration occurred at the center of the quarry and was about 4,957 µg/m3. During the summer season, the maximum one-hour average PM10 concentration occurred at the center of the quarry and was about 4,970 µg/m3. All the observed PM10 concentrations during the summer and winter seasons exceeded the allowable one-hour average concentration of the Environmental Protection Agency (EPA) which is 365.21 µg/m3.

Evaluating the Performance of Random Forest and Multiple Linear Regression for Higher Observed PM10 Concentrations

Background: Air pollution is notable for its direct impact on human health. Hence, the ability to accurately predict air pollution concentrations is vital to raising public awareness of this issue and for better understanding of air quality management. Aim: Therefore, the aim of this research is to predict PM10 concentrations in Malaysia, specifically on Langkawi Island using random forest and multiple linear regression. Method: The predictive analytics were based on air pollution hourly data from 2003 until 2017. The eight parameters chosen in this study were PM10, NO2, O3, CO, SO2, Relative Humidity (RH), Temperature (T), and Wind Speed (WS). The findings revealed that PM10, SO2, NO2, CO, and O3 hourly trends at Langkawi Island were below the recommended Malaysian Ambient Air Quality Guidelines (MAAQG) standard. Multiple linear regression (MLR) and random forest (RF) were used for modelling and compared based on their prediction accuracy. Result: The values of RMSE, NAE, IA, PA and R2 for MLR were 8.0698, 0.1368, 0.8584, 0.7737 and 0.5984 respectively while the values of RMSE, NAE, IA, PA and R2 for RF were 6.674038, 0.107664, 0.911974, 0.852570 and 0.726681 correspondingly. From the results, the RF method was chosen as a better model than MLR since both; the error measures and the accuracy measures results are close to 1. Nevertheless, the PM10 models (RF and MLR) are unable to take into account the higher observed concentrations.

Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area

Abstract. Air pollution is one of the major challenges in urban areas. It can have a major impact on human health and society and is currently a subject of several litigations in European courts. Information on the level of air pollution is based on near-surface measurements, which are often irregularly distributed along the main traffic roads and provide almost no information about the residential areas and office districts in the cities. To further enhance the process understanding and give scientific support to decision makers, we developed a prototype for an air quality forecasting system (AQFS) within the EU demonstration project “Open Forecast”. For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF-Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a turbulence-permitting resolution of 50 m were used, and a single-layer urban canopy model was active in all domains. As a demonstration case study, 21 January 2019 was selected, which was a heavily polluted day with observed PM10 concentrations exceeding 50 µg m−3. Our results show that the model is able to reasonably simulate the diurnal cycle of surface fluxes and 2 m temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in Stuttgart. The simulated fields of particulates with a diameter of less than 10 µm (PM10) and nitrogen dioxide (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, AQFS will serve as a valuable tool for air quality forecasting and has the potential of being applied to other cities around the world.

Analysis of Weather Patterns Related to Wintertime Particulate Matter Concentration in Seoul and a CMIP6-Based Air Quality Projection

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014.

The transport of PM10 over Cape Town during high pollution episodes

This study investigates the influence of PM10 from remote sources on PM10 episodes over the city of Cape Town. For the study, we analysed observation data from Cape Town's air quality monitoring stations as well as high-resolution simulation data from the Weather Research and Forecasting model with Chemistry (WRF-Chem). The observation data were used to identify PM10 episodes during the study period (2008–2014) and WRF-Chem was applied to simulate the atmospheric conditions and PM10 transport over southern Africa during each episode. The capability of WRF-Chem to simulate the wind and PM10 concentration over Cape Town was quantified and the paths of air parcels over Cape Town during each episode were tracked. Results of the study show that WRF-Chem gives a realistic simulation of observed wind (speed and direction) over the city during the episodes, but the model struggles to reproduce the observed PM10 concentration. For most episodes, the magnitude of the simulated PM10 is lower than the observed due to lack of local emissions in the simulations. In some cases, the model reproduces the peak in PM10 concentration some days earlier or later than observed. The simulations show that most air parcels over Cape Town during the episodes have travelled over major dust source regions (i.e., the Kalahari or Namib Desert) before reaching the city. Most of episodes are associated with a southward transport of a plume of PM10 from the north-west coast of southern Africa to Cape Town. This PM10 plume is induced by a coastal trough and a continental high pressure system. In some cases, local topography influences the intrusion of the PM10 plume into Cape Town by blocking some of the pollution, thereby minimising the amount of PM10 that reaches the city. Results of the study suggest that the transport of PM10 from the north-west coast of southern Africa may contribute to PM10 episodes in Cape Town.

The dynamical impact of Rossby wave breaking upon UK PM<sub>10</sub> concentration

Abstract. Coarse particulate matter (PM10) has long been understood to be hazardous to human health, with mortality rates increasing as a result of raised ground level concentrations. We explore the influence of synoptic-scale meteorology on daily mean observed PM10 concentration ([PM10]) using Rossby wave breaking (RWB). Meteorological reanalysis data for the winter months (DJF) between January 1999 and December 2008 and observed PM10 data for three urban background UK (Midland) sites were analysed. Three RWB diagnostics were used to identify RWB that had significant influence on UK Midland PM10. RWB events were classified according to whether the RWB was cyclonic or anticyclonic in its direction of breaking and whether the RWB event was influenced more by poleward or equatorial air masses. We find that there is a strong link between RWB events and UK [PM10]. Significant increases (p < 0.01) in UK [PM10] were seen 1 day following RWB occurring in spatially constrained northeast Atlantic–European regions. Analysis into episodic PM10 exceedance events shows increased probability of [PM10] exceedance associated with all RWB subsets. The greatest probability of exceeding the UK [PM10] threshold was associated with cyclonic RWB preceded by anticyclonic RWB forming an Ω block synoptic pattern. This mechanism suggests an easterly advection of European PM10 followed by prolonged stagnant conditions within the UK and led to an almost threefold increase in the probability of the UK Midlands exceeding a hazardous [PM10] threshold (0.383), when compared to days where no RWB was detected (0.129).

Impact assessment of PM10 cement plants emissions on urban air quality using the SCIPUFF dispersion model.

The Second-order Closure Integrated Puff (SCIPUFF) model was used to study the impact on urban air quality caused by two cement plants emissions located near the city of Caserta, Italy, during the entire year of 2015. The simulated and observed PM10 concentrations were compared using three monitoring stations located in urban and sub-urban area of Caserta city. Both simulated and observed concentrations are shown to be highest in winter, lower in autumn and spring and lowest in summer. Model results generally follow the pattern of the observed concentrations but have a systematic under-prediction of the concentration values. Measures of the bias, NMSE and RMSE indicate a good correlation between observed and estimated values. The SCIPUFF model data analysis suggest that the cement plants are major sources for the measured PM10 values and are responsible for the deterioration of the urban air quality in the city of Caserta.

Temporal and spatial variations of particulate matter and gaseous pollutants in the urban area of Tehran

Being hemmed in on two sides by high mountains, the urban area of Tehran is characterized by high levels of particulate matter and gaseous pollutants, which have adverse consequences on human health, ecosystems and environment. Using air quality measurements taken in different regions of Tehran, spatial and temporal variations of particulate matter and gaseous pollutants are analyzed to identify the typical climatological aspects of air pollutants. In terms of particulate matter concentrations, South Tehran is more polluted than Central to North Tehran, while West Tehran is more polluted than the East. Concentrations of particles in North Tehran are lower in the midday compared to the midnight, whereas the opposite is true in South Tehran. The observed annual mean concentrations of PM2.5 and PM10 in North Tehran were 37.5 and 76.3 μg m−3, respectively, which are substantially greater than the national annual mean safety limits of 10 μg m−3 for PM2.5 and 20 μg m−3 for PM10. The observed high levels of particulate matter underline the essential need for a coordinated action to reduce the rapidly increasing air pollution over the growing urban area of Tehran. Noticeable monthly (seasonal) variations are evident in the observed PM10 concentrations, with a minimum of 61.5 μg m−3 in March (spring) and a maximum of 82.9 μg m−3 in July (summer), reflecting contribution of weather conditions. Analyzing daily PM2.5 (PM10) concentrations indicate that mid-week Wednesdays (Mondays) are the most polluted days. The higher mid-week concentrations reflect contribution of heavy vehicular traffic, industrial operation and increased commercial activities. Strong diurnal variations in the concentrations of particulate matter in North Tehran are detected, varying from a peak in late night to a minimum in late afternoon, indicating contribution of deeper daytime convective boundary layer and stronger winds in dispersion of particles.

Forecasting of Asian dust storm that occurred on May 10–13, 2011, using an ensemble-based data assimilation system

An ensemble-based assimilation system that used the MASINGAR mk-2 (Model of Aerosol Species IN the Global AtmospheRe Mark 2) dust forecasting model and satellite-derived aerosol optical thickness (AOT) data, processed in the JAXA (Japan Aerospace Exploration Agency) Satellite Monitoring for Environmental Studies (JASMES) system with MODIS (Moderate Resolution Imaging Spectroradiometer) observations, was used to quantify the impact of assimilation on forecasts of a severe Asian dust storm during May 10–13, 2011. The modeled bidirectional reflectance function and observed vegetation index employed in JASMES enable AOT retrievals in areas of high surface reflectance, making JASMES effective for dust forecasting and early warning by enabling assimilations in dust storm source regions. Forecasts both with and without assimilation were validated using PM10 observations from China, Korea, and Japan in the TEMM WG1 dataset. Only the forecast with assimilation successfully captured the contrast between the core and tail of the dust storm by increasing the AOT around the core by 70–150% and decreasing it around the tail by 20–30% in the 18-h forecast. The forecast with assimilation improved the agreement with observed PM10 concentrations, but the effect was limited at downwind sites in Korea and Japan because of the lack of observational constraints for a mis-forecasted dust storm due to cloud.

A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval

Satellite measurements have been widely used to estimate particulate matters (PMs) on the ground and their effects on human health. However, such estimation is susceptible to meteorological conditions and may result in large errors. In this study, we developed a nonlinear empirical model for seasonal ground-level PM10 prediction in Xi'an, Shaanxi province of northwestern China. The nonlinear model is based on 3years (2011–2013) of daily PM10 concentration data from 13 PM10 monitoring stations in Xi'an, aerosol optical depth (AOD) data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), surface meteorological measurements, and NCEP/NCAR reanalysis data. The nonlinear model corrects the AOD data using the height of plenary boundary layer and surface relative humidity, and further adjusts the corrected AOD according to visibility, surface temperature and surface wind speed. Our results show that there is almost a threefold improvement from 0.28 to 0.78 in the correlation coefficient when using the nonlinear model compared to using a linear regression model of AOD and PM10. The root-mean-square error (RMSE) is reduced from 34.42 to 21.33μg/m3 using the nonlinear model over the linear model. Further analysis about meteorological variables shows that relative humidity and visibility are important factors to improve the relationship between AOD and PM10. The relationship between the predicted PM10 concentration from the nonlinear model and observed PM10 concentration is the best in winter, moderate in autumn and spring, and poor in summer. Further validation has shown that the nonlinear model is able to explain approximately 79% (R2=0.79, n=270, p<0.01) of the variability in the monthly-mean PM10 concentration with an RMSE of 11.7μg/m3 and mean absolute percentage error of 14.2% based on monthly-mean data set. These results are useful for accessing surface PM10 concentration and monitoring regional air pollution.

Application of WRF-Chem to forecasting PM&lt;SUB align="right"&gt;10 concentration over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one-way nested domains using the GFS meteorological data and the TNO MACC II emissions. The 48 hour forecasts were run for each day of the winter and summer period of 2014 and there is only a small decrease in model performance for winter with respect to forecast lead time. The model in general captures the variability in observed PM10 concentrations for most of the stations. However, for some locations and specific episodes, the model performance is poor and the results cannot yet be used by official authorities. We argue that a higher resolution sector-based emission data will be helpful for this analysis in connection with a focus on planetary boundary layer processes in WRF-Chem and their impact on the initial distribution of emissions on both time and space.

The effect of SRTM and Corine Land Cover data on calculated gas and PM10 concentrations in WRF-Chem

The goal of this study is to investigate the impact of the high resolution Shuttle Radar Topography Mission (SRTM) 90 m × 90 m topography data, together with the 100 m × 100 m resolution Corine Land Cover 2006 on the simulated gas and particulate matter (PM10) concentrations by WRF-Chem. We focused our analysis on the well-known highly urbanized region of the Po Valley. Large differences are found in the geographical distribution of the land cover classes between Corine Land Cover and 30 arc seconds USGS. The simulation with the SRTM and Corine Land Cover increases modelled temperature at 2 m and reduces wind speeds due to more friction at the surface induced by the Corine Land Cover. Latent and sensible heat fluxes show large differences between the two simulations and the related boundary layer development and depth. The simulation with the SRTM and Corine Land Cover favours the precipitation amount over a large of part the Alps and follows the pattern of the difference in topography between the two topography data sets. In term of air quality indicators, impacts are also large and geographical dependent. Monthly average of CO, NO and SO2 concentrations over a large part of the Po Valley are higher when using Corine Land Cover, up to ∼20, ∼50 and ∼55%, respectively. With respect to PM10, the impacts are also geographical dependent. Over the Po valley area, calculated PM10 concentrations are in general higher using Corine Land Cover (up to 6.7 ug/m3 [∼26%] westerly of Milan) while differences are smaller over the Alps (∼0.25ug/m3 [∼20%]). Although the scope of this work is not to evaluate the model performance in calculated meteorological parameters and gas and PM10 concentrations, calculated values by the simulation with SRTM and Corine Land Cover show a better agreement with the observations than the simulation with the USGS topography and land cover data sets. A quantitative comparison between modelled and observed monthly average PM10 concentrations shows that both simulations underestimate the observed PM10 concentrations by a factor ∼4. The agreement is much better during episodes for the simulation with the SRTM and Corine Land Cover. For CO, SO2 and NOx, the modelled monthly mean concentrations are similar for the two simulations. Larger differences are found during some episodes and regions with the SRTM and Corine LC simulation being in better agreement with the observations.

The transport and deposition of dust and its impact on phytoplankton growth in the Yellow Sea

Observed surface PM10 mass concentrations and weather records, satellite-derived aerosol and ocean color data, dust simulations, and a backward trajectory analysis were used to investigate a severe dust storm episode during 19–22 March 2010 and its impact on phytoplankton growth in the Yellow Sea. The observed PM10 concentrations and weather records showed that heavy dust pollution occurred along the transport pathway. The high MODIS AOD regions were consistent with the simulated high dust emission and deposition regions. Based on the CALIPSO satellite observations, the high dust aerosol layer was below 2 km above ground, indicating the dust transported to the sea was centered within the lower layer. The model simulation estimated that the total deposition flux over the southern Yellow Sea during 19–22 March 2010 was about 1.5 g m−2. Consequently, the chlorophyll a concentration was calculated to have increased four-fold. Ten to thirteen days later, a phytoplankton bloom occurred. The iron deposited by the severe dust episode could have increased the chlorophyll a concentration in the southern Yellow Sea by 10–68%. Our results suggest that severe dust storms containing readily bioavailable nutrients may enhance phytoplankton growth in the southern Yellow Sea.

An empirical method of RH correction for satellite estimation of ground-level PM concentrations

A hygroscopic growth model suitable for local aerosol characteristics and their temporal variations is necessary for accurate satellite retrieval of ground-level particulate matters (PM). This study develops an empirical method to correct the relative humidity (RH) impact on aerosol extinction coefficient and to further derive PM concentrations from satellite observations. Not relying on detailed information of aerosol chemical and microphysical properties, this method simply uses the in-situ observations of visibility (VIS), RH and PM concentrations to characterize aerosol hygroscopicity, and thus makes the RH correction capable of supporting the satellite PM estimations with large spatial and temporal coverage.In this method, the aerosol average mass extinction efficiency (αext) is used to describe the general hygroscopic growth behaviors of the total aerosol populations. The association between αext and RH is obtained through empirical model fitting, and is then applied to carry out RH correction. Nearly one year of in-situ measurements of VIS, RH and PM10 in Beijing urban area are collected for this study and RH correction is made for each of the months with sufficient data samples. The correlations between aerosol extinction coefficients and PM10 concentrations are significantly improved, with the monthly correlation R2 increasing from 0.26–0.63 to 0.49–0.82, as well as the whole dataset's R2 increasing from 0.36 to 0.68. PM10 concentrations are retrieved through RH correction and validated for each season individually. Good agreements between the retrieved and observed PM10 concentrations are found in all seasons, with R2 ranging from 0.54 in spring to 0.73 in fall, and the mean relative errors ranging from −2.5% in winter to −10.8% in spring. Based on the satellite AOD and the model simulated aerosol profiles, surface PM10 over Beijing area is retrieved through the RH correction. The satellite retrieved PM10 and those observed at ground sites agree well with each other, with R2 = 0.46 and a relative error of 19.3%.

Case study of the Asian dust and pollutant event in spring 2006: Source, transport, and contribution to Taiwan

Surface measurements and a regional dust model were used to analyze the source, transport, and contribution of a dust event transporting with aerosol pollutant over Taiwan from 16 to19 March, 2006. During the event, the hourly aerosol concentrations reached close to 400μgm−3 in northern Taiwan, and approximately 300μgm−3 in other areas of the island. Trajectory and regional dust models show that the dust event originated in eastern Mongolia and northern China, and the dust layer can descend from 2 to 3km in the source area to below 1.5km over Taiwan. On the other hand, model results show that pollution was transported near the surface from coastal China to Taiwan. During this dust event, polluted aerosol was first observed over northern Taiwan right after a frontal passage, and the concentration was strongly enhanced following the passage of the light rainfall 12h later. The descent of dusty air from the free troposphere lagged the arrival of polluted air by 7h, and was partially mixed with polluted aerosol when the transport decelerated over Taiwan.During the event, dust particles accounted for up to 60% of observed particulate matter less than 10μm (PM10) over Taiwan, but decreased to less than 35% for particulate matter less than 2.5μm (PM2.5) over most areas of the island. On the other hand, the long-range transport of non-dust aerosols, mainly anthropogenic pollutants, accounted for close to 30% of observed PM10 concentration in northern and western Taiwan prior to dust arrival, and the contribution of PM2.5 increased to close to 40% over the same areas. Local emission of aerosols accounted for less than 25% of PM10 concentrations in northern Taiwan, but was about 60% for PM2.5 in central and southern Taiwan because these areas are less influenced by long-range transport.

Effect of dead leaves on early spring dust emission in East Asia

Dead leaves remaining after the growing season can suppress dust emission by wind erosion, but this has not been considered in numerical models of dust emission. We investigated the effect of dead leaves by analyzing surface meteorological data and numerical simulations for early spring Asian dust events, and propose a parameterization of the dead-leaf effect. World Meteorological Organization (WMO) synoptic data indicate that high vegetation cover from the previous year can increase the threshold friction velocity for early spring of the following year. The reduction rate of dead leaves is calculated using 10-year averaged monthly vegetation cover data derived from the Normalized Difference Vegetation Index (NDVI), assuming that the amount of dead leaves decreases linearly, and the amount of dead leaves is included in calculating threshold friction velocity. To investigate the effect of dead leaves in simulations of Asian dust events, case studies were carried out with and without the dead-leaf effect for early spring Asian dust events using a Weather Research and Forecasting with Chemistry (WRF-Chem) model with a new dust emission scheme. Including the dead-leaf effect improved the model results, and the modeled surface concentrations are similar to the observed PM10 concentrations.

동아시아지역에서 GEOS-Chem에 의한 경계조건이 CMAQ 모사 결과에 미치는 영향에 대한 평가

The present work is an attempt to improve the performance of a regional air quality model by means of liking it with a global chemistry transport model. The global chemical transport model of GEOS-Chem is used to provide BC (Boundary Condition)s which reflect temporal and spatial variations at boundaries of regional chemical transport model of CMAQ over East Asia. First, GEOS-Chem outputs are evaluated by comparing predicted concentrations with observed monthly data of gas phase species and secondary inorganic aerosols from EANET (Acid Deposition Monitoring Network in East Asia) sites. The results show that predicted PM10 concentrations are in good agreement with the observations. This implies that GEOS-Chem outputs could be used to provide BCs to CMAQ. Simulated daily and monthly mean PM10 concentrations of CMAQ with the linkage of GEOS-Chem`s BCs and constant BCs are then evaluated by comparing predicted concentrations with observations at API (Air Pollution Index) sites in China as well as EANET sites in Korea. CMAQ with the GEOS-Chem outputs improves model simulation in depicting observed PM10 concentrations comparing with those with constant BCs. It is also found that influence of aerosol species are largely dependent on the BCs over East Asia and Korea. Mean biases between simulated versus observed daily and monthly mean concentrations of PM10 with the GEOS-chem were improved by 1~8 in China region, 3.26 in Korea.

How much are urban residents in Mexico willing to pay for cleaner air?

AbstractWe estimate the marginal willingness-to-pay for PM10 abatement in the three largest Mexican cities. We use a unique data set with actual market transactions at the household level from January 2003 to May 2004 and observed PM10 concentrations. We follow an instrumental variable approach to mitigate bias from omitted variables. We exploit the seasonality in PM10 concentrations due to rainfall patterns in those cities to construct a valid instrument for PM10. We find the house price–pollution elasticity to be around − 0.07 for Mexico City, − 0.05 for Guadalajara, and − 0.07 for Monterrey, implying that one unit reduction in PM10 levels is valued at US$41.73, 36.34 and 43.47, respectively. Our results indicate that urban residents of Mexico are willing to pay for cleaner air.