Monitoring and source apportionment of particulate matter pollution in Bangkok metropolitan region by receptor modeling | |
| Author | Uaeaungkool Mahawong |
| Call Number | AIT Thesis no.EV-17-30 |
| Subject(s) | Air pollution. Air quality management. Climatic changes--Thailand Environmental aspects |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. EV-17-30 |
| Abstract | One of the important air pollutants is Particulate Matter which has high levels in many urban and suburban areas of Thailand. The monitoring was conducted for 2 sites in the Pollution Control Department (PCD) to present the urban area of Bangkok province, and at the Asian Institute of Technology (AIT) to represent the suburb area. The weekly PM mass concentrations were comparative at PCD and AIT sites. The PM2.5 and PM>2.5 average concentrations were 15 ± 6 μg/m³ and 38 ± 17μg/m³ in wet periods and higher, 28 ± 10 μg/m³ and 41 ± 15 μg/m³, in the dry periods at PCD site. The PM mass concentrations of PM2.5 and PM>2.5 at AIT site were 15 ± 5 μg/mm³ and 37 ± 16μg/m³ in wet periods and 32 ± 11 μg/m³ and 44 ± 18 μg/m³ in the dry periods. The Black Carbon (BC) measured by optical method was correlated to EC by TOR. The average of EC and OC concentrations for PCD site of PM2.5 were 2.75 ± 1.44 μg/m³ and 4.29 ± 3.34 μg/m, respectively, while those in PM>2.5 were 0.84±0.55 μg/m³, and 1.80±0.67 μg/m³, respectively. At AIT site, the average EC and OC concentrations in PM2.5, were 3.60 ± 2.19 μg/m³ and 5.52 ± 4.59 μg/m³ while those in coarse PM were 1.07 ±0.57 μg/m³ and 2.40 ±1.97μg/m³, respectively. At both sites, the most dominant anion species for PM2.5 was SO42, while that of cation was NH4+. In the coarse fraction, the largest anion was NO3-, while Ca2+ was the largest cation. Totally, 40 elements were analyzed from quartz filter samples of PM2.5 and PM>2.5 by ICP-MS. At both sampling sites, the largest was for 54Fe with the level in PM2.5 and PM>2.5 in wet period and dry period of 2.03 μg/m³ and 2.95 μg/m³ at PCD site, and the corresponding values at AIT were 2.29μg/m³ and 3.81μg/m³, respectively. The results of reconstructed mass for PM2.5 showed that the significant groups groups of mass for both sampling sites were Organic Matter from biomass burning, secondary inorganic aerosol, and soot. The Chemical Mass Balance (CMB8.2) results showed that the largest contributor to PM2.5 in the wet period at PCD site was exhaustion from diesel vehicles (28%of PM mass), biomass burning (25%) and (NH4)2SO4(20%). In the dry periods, the biomass burning was the highest contributor (34%) followed by diesel vehicles (23%), and (NH4)2SO4(17%). Similarly, at AIT, the largest contributor to PM2.5 in wet periods was diesel vehicles (31%), biomass burning (21%) and (NH4)2SO4(21%) while in the dry period the biomass burning was the highest contributor (36%), followed by diesel vehicles from traffic(26%) and (NH4)2SO4(15%). Other sources such as industry, soil/road dust etc. had minor contributions. The Positive Matrix Factorization analysis produced preliminary results for PM2.5 at PCD site which showed the highest contributor in wet periods was secondary aerosol (24%), followed by soil/road dust (23%). In the dry period the Industrial 1 (Ni and Zn rich)was the highest contributor (23%) followed by biomass burning (20%). The resulting source profiles of PMF were not explainable hence suggesting further scrutinizing the ambient data sets is still required. The results of HYSPLIT back trajectories for the high PM2.5 week at both sites showed the air mass trajectories with pathways over the continental areas with regional effect and also stagnant conditions along those pathways. The week of lower PM2.5 at both sites, the origination of the air mass trajectories was mainly from the ocean with a long marine pathway. Further in depth analysis of the data is still required to produce the input for receptor modeling including the PMF. A longer period is required to produce a long term data series for understanding the situation of PM pollution in BMR to inform policy making to reduce the PM levels. |
| Year | 2017 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Environmental Engineering (EV) |
| Chairperson(s) | Nguyen, Thi Kim Oanh; |
| Examination Committee(s) | Thammarat Koottatep;Prapat Pongkiatkul;Sato, Keiichi; |
| Scholarship Donor(s) | Royal Thai Government Fellowship; |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2017 |