Proximity analysis of air pollution exposure and its potential risk

Proximity analysis and spatial variability of the ambient nitrogen dioxide (NO(2)) concentration in Rayong province, Thailand, were analyzed using geostatistics and spatial modeling techniques. Annual concentrations of nitrogen dioxide were predicted and spatially interpolated using various interpolation techniques (i.e. kriging, IDW and spline). Sensitivity analysis was carried out to assure the accuracy of the predicted results. The GIS-based exposure map was simulated and was assisted to identify high exposure areas. A health risk warning system was set for "action" (exceeds 100% of the annual average NO(2) guideline), for "alert" (between 66-100% of the annual average NO(2) guideline) and for "some concern" (between 33-66% of the annual average NO(2) guideline). Although no areas were exposed to an action level, many locations in the study area could have levels of "some concern". Potential risk to the population was analyzed by spatial interpolation of the nitrogen dioxide concentration with population data. The result indicated the number of people exposed to air pollution, as well as the areas which have a high risk to air pollution. About 88.3% of the total population in the study area live in areas where levels of air pollution are designated as being in the "some concern" zone. About 6.7% of the registered population have their residence in an area where action should be taken for air quality management. The study demonstrates the application of GIS-based prediction for the evaluation of exposure mapping, in order to determine the spatial extent and frequency of areas where pollution levels exceed target values, and their potential health impacts.     

Roadside Particulate Air Pollution in Bangkok

Abstract

Airborne fine particles of PM_2.5-10 and PM_2.5 in Bangkok, Nonthaburi, and Ayutthaya were measured from December 22, 1998, to March 26, 1999, and from November 30, 1999, to December 2, 1999. Almost all the PM₁₀ values in the high-polluted (H) area exceeded the Thailand National Ambient Air Quality Standards (NAAQS) of 120 μg/m³. The low-polluted (L) area showed low PM₁₀ (34–74 μg/m³ in the daytime and 54–89 μg/m³ at night). PM_2.5 in the H area varied between 82 and 143 μg/m³ in the daytime and between 45 and 146 μg/m³ at night. In the L area, PM_2.5 was quite low both day and night and varied between 24 and 54 μg/m³, lower than the U.S. Environmental Protection Agency (EPA) standard (65 μg/m³). The personal exposure results showed a significantly higher proportion of PM_2.5 to PM₁₀ in the H area than in the L area (H = 0.80 ± 0.08 and L = 0.65 ± 0.04).

Roadside PM₁₀ was measured simultaneously with the Thailand Pollution Control Department (PCD) monitoring station at the same site and at the intersections where police work. The result from dual simultaneous measurements of PM₁₀ showed a good correlation (correlation coefficient: r = 0.93); however, PM levels near the roadside at the intersections were higher than the concentrations at the monitoring station. The relationship between ambient PM level and actual personal exposures was examined. Correlation coefficients between the general ambient outdoors and personal exposure levels were 0.92 for both PM_2.5 and PM₁₀.

Bangkok air quality data for 1997–2000, including 24-hr average PM₁₀, NO₂, SO₂, and O₃ from eight PCD monitoring stations, were analyzed and validated. The annual arithmetic mean PM₁₀ of the PCD data at the roadside monitoring stations for the last 3 years decreased from 130 to 73 μg/m³, whereas the corresponding levels at the general monitoring stations decreased from 90 to 49 μg/m³. The proportion of days when the level of the 24-hr average PM₁₀ exceeded the NAAQS was between 13 and 26% at roadside stations. PCD data showed PM₁₀ was well correlated with NO₂ but not with SO₂, suggesting that automobile exhaust is the main source of the particulate air pollution. The results obtained from the simultaneous measurement of PM_2.5 and PM₁₀ indicate the potential environmental health hazard of fine particles. In conclusion, Bangkok traffic police were exposed to high levels of automobile-derived particulate air pollution.     

Roadside particulate air pollution in Bangkok

Airborne fine particles of PM(2.5-10) and PM2.5 in Bangkok, Nonthaburi, and Ayutthaya were measured from December 22, 1998, to March 26, 1999, and from November 30, 1999, to December 2, 1999. Almost all the PM10 values in the high-polluted (H) area exceeded the Thailand National Ambient Air Quality Standards (NAAQS) of 120 microg/m3. The low-polluted (L) area showed low PM10 (34-74 microg/m3 in the daytime and 54-89 microg/m3 at night). PM2.5 in the H area varied between 82 and 143 microg/m3 in the daytime and between 45 and 146 microg/m3 at night. In the L area, PM2.5 was quite low both day and night and varied between 24 and 54 microg/m3, lower than the U.S. Environmental Protection Agency (EPA) standard (65 microg/m3). The personal exposure results showed a significantly higher proportion of PM2.5 to PM10 in the H area than in the L area (H = 0.80 +/- 0.08 and L = 0.65 +/- 0.04). Roadside PM10 was measured simultaneously with the Thailand Pollution Control Department (PCD) monitoring station at the same site and at the intersections where police work. The result from dual simultaneous measurements of PM10 showed a good correlation (correlation coefficient: r = 0.93); however, PM levels near the roadside at the intersections were higher than the concentrations at the monitoring station. The relationship between ambient PM level and actual personal exposures was examined. Correlation coefficients between the general ambient outdoors and personal exposure levels were 0.92 for both PM2.5 and PM10. Bangkok air quality data for 1997-2000, including 24-hr average PM10, NO2, SO2, and O3 from eight PCD monitoring stations, were analyzed and validated. The annual arithmetic mean PM10 of the PCD data at the roadside monitoring stations for the last 3 years decreased from 130 to 73 microg/m3, whereas the corresponding levels at the general monitoring stations decreased from 90 to 49 microg/m3. The proportion of days when the level of the 24-hr average PM10 exceeded the NAAQS was between 13 and 26% at roadside stations. PCD data showed PM10 was well correlated with NO2 but not with SO2, suggesting that automobile exhaust is the main source of the particulate air pollution. The results obtained from the simultaneous measurement of PM2.5 and PM10 indicate the potential environmental health hazard of fine particles. In conclusion, Bangkok traffic police were exposed to high levels of automobile-derived particulate air pollution.     

NO2 and SO2dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area,Thailand

Map Ta Phut industrial area (MA) is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. Air quality management for the area is known to be difficult, due to lack of understanding of how emissions from different sources or sectors (e.g., industrial, power plant, transportation, and residential) contribute to air quality degradation in the area. In this study, a dispersion study of NO₂ and SO₂ was conducted using the AERMOD model. The area-specific emission inventories of NO_x and SO₂ were prepared, including both stack and nonstack sources, and divided into 11 emission groups. Annual simulations were performed for the year 2006. Modeled concentrations were evaluated with observations. Underestimation of both pollutants was found, and stack emission estimates were scaled to improve the modeled results before quantifying relative roles of individual emission groups to ambient concentration over four selected impacted areas (two are residential and the others are highly industrialized). Two concentration measures (i.e., annual average area-wide concentration or AC, and area-wide robust highest concentration or AR) were used to aggregately represent mean and high-end concentrations for each individual area, respectively. For AC-NO₂, on-road mobile emissions were found to be the largest contributor in the two residential areas (36–38% of total AC-NO₂), while petrochemical-industry emissions play the most important role in the two industrialized areas (34–51%). For AR-NO₂, biomass burning has the most influence in all impacted areas (>90%) except for one residential area where on-road mobile is the largest (75%). For AC-SO₂, the petrochemical industry contributes most in all impacted areas (38–56%). For AR-SO₂, the results vary. Since the petrochemical industry was often identified as the major contributor despite not being the largest emitter, air quality workers should pay special attention to this emission group when managing air quality for the MA.

Implications: Effective air quality management in Map Ta Phut Industrial Area, Thailand requires better understanding of how emissions from various sources contribute to the degradation of ambient air quality. Based on the dispersion study here, petrochemical industry was generally identified as the major contributor to ambient NO₂ and SO₂. By accounting for all stack and non-stack sources, on-road mobile emissions were found to be important in some particular areas.     

NO2 and SO2 dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

Map Ta Phut industrial area (MA) is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. Air quality management for the area is known to be difficult, due to lack of understanding of how emissions from different sources or sectors (e.g., industrial, power plant, transportation, and residential) contribute to air quality degradation in the area. In this study, a dispersion study of NO2 and SO2 was conducted using the AERMOD model. The area-specific emission inventories of NOx and SO2 were prepared, including both stack and nonstack sources, and divided into 11 emission groups. Annual simulations were performed for the year 2006. Modeled concentrations were evaluated with observations. Underestimation of both pollutants was Jbund, and stack emission estimates were scaled to improve the modeled results before quantifying relative roles of individual emission groups to ambient concentration overfour selected impacted areas (two are residential and the others are highly industrialized). Two concentration measures (i.e., annual average area-wide concentration or AC, and area-wide robust highest concentration or AR) were used to aggregately represent mean and high-end concentrations Jbfor each individual area, respectively. For AC-NO2, on-road mobile emissions were found to be the largest contributor in the two residential areas (36-38% of total AC-NO2), while petrochemical-industry emissions play the most important role in the two industrialized areas (34-51%). For AR-NO2, biomass burning has the most influence in all impacted areas (>90%) exceptJor one residential area where on-road mobile is the largest (75%). For AC-SO2, the petrochemical industry contributes most in all impacted areas (38-56%). For AR-SO2, the results vary. Since the petrochemical industry was often identified as the major contributor despite not being the largest emitter, air quality workers should pay special attention to this emission group when managing air quality for the MA.