Integrated Sustainable Management of Petrochemical Industrial Air Pollution.

The emission inventory, emission factor, and spatial concentration distribution of volatile organic compounds (VOCs) from a petrochemical industry (aromatics plant) were intensively evaluated in this study to elucidate the potential sources of BTX emission and their contribution to ambient concentrations. Five emission groups were quantified through direct measurement and emission models. These data were then used as input for the AERMOD dispersion model for the source apportionment analysis. The source to ambient contribution analysis revealed that a wastewater treatment facility and organic liquid storage tank were major contributors accounting for about 20.6-88.4% and 10.3-75.4% to BTX environmental concentrations, respectively. The highest annual ambient concentrations of benzene (B), toluene (T), and xylenes (X) were predicted as 9.0, 2.8, and 57.9 µg/m3 at the fence line of the plant boundary, respectively. These findings assist policymakers in prioritizing the appropriate control measures to the right source by considering not just the amount released but also their contribution to ambient concentrations. This study suggested that the wastewater treatment unit should be changed to the closed system which will benefit reduction in its emission (45.05%) as well as effectively minimizing ambient VOC concentration by 49.96% compared to its normal operation.

Formation potential and source contribution of secondary organic aerosol from volatile organic compounds.

Secondary organic aerosol (SOA), a key constituent of fine particulate matter, can be formed through the oxidation of volatile organic compounds (VOCs). However, information on its relevant emission sources remains limited in many cities, especially concerning different types of land use. In this study, VOC concentration in Bangkok Metropolitan Region (BMR), Thailand, was continuously collected for 24 h by 6-L evacuated canister and analyzed by gas chromatography/mass spectrophotometry following USEPA TO15, and the formation of SOA was evaluated through the comprehensive direct measurements and speciation of ambient VOCs. Finally, source contribution of VOCs to SOA formation was characterized using the Positive Matrix Factorization (PMF) model. The results revealed the abundant group of VOCs species in the overall BMR was oxygenated VOCs, accounting for 49.97-57.37%. The SOA formation potential (SOAP) ranged from 1,134.33 to 3,143.74 µg m-3 . The VOC species contributing to the highest SOAP was toluene. Results from the PMF model revealed the dominant emission source of VOCs that greatly contributed to SOA was vehicle exhaust emission. Industrial combustion was the main source of VOC emission contributing to SOA in industrial areas. Sources of fuel evaporation, biomass burning, and cooking were also found in the study areas but in small quantities. The results of this study elucidated that different emission sources of VOCs contribute to SOA with respect to different types of land use. Findings of this study highlight the necessity to identify the contribution of potential emission sources of SOA precursors to effectively manage urban air pollution.

Emission losses and dispersion of volatile organic compounds from tank farm of petroleum refinery complex.

Emission characteristics of volatile organic compounds (VOC) emitted from the tank farm of petroleum refinery were evaluated in this study in order to analyze for the potential impacts on health and odor nuisance problems. Estimation procedures were carried out by using the U.S.EPA TANK 4.0.9d emission model in conjunction with direct measurements of gas phase of each stored liquid within aboveground storage tanks. Results revealed that about 61.12% of total VOC emitted from the tank farm by volume were alkanes, in which pentane were richest (27.4%), followed by cyclopentane (19.22%), propene (19.02%), and isobutene (14.22%). Mostly of pentane (about 80%) were emitted from the floating roof tanks contained crude oil corresponded to the largest annual throughput of crude oil as compared with other petroleum distillates. Emission data were further analyzed for their ambient concentration using the AERMOD dispersion model in order to determine the extent and magnitude of odor and health impacts caused by pentane. Results indicated that there was no health impact from inhalation of pentane. However, predicted data were higher than the odor threshold values of pentane which indicated the possibility of odor nuisance problem in the vicinity areas of the refinery. In order to solve this problem, modification of the type of crude oil storage tanks from external floating roof to domed external floating roof could be significant success in reduction of both emissions and ambient concentrations of VOC from petroleum refinery tank farm.

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.

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.

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.