{"created":"2023-05-15T12:55:48.817126+00:00","id":845,"links":{},"metadata":{"_buckets":{"deposit":"f6280ee4-799b-4dad-9d9b-789f2935d4a7"},"_deposit":{"created_by":18,"id":"845","owners":[18],"pid":{"revision_id":0,"type":"depid","value":"845"},"status":"published"},"_oai":{"id":"oai:jicari.repo.nii.ac.jp:00000845","sets":["602","602:627"]},"author_link":["1098"],"item_10008_date_8":{"attribute_name":"出版年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2017-10-01","subitem_date_issued_type":"Issued"}]},"item_10008_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"This final report of the joint research project “A study in urban air pollution improvement in Asia” is submitted by the Asian Institute of Technology (AIT) on behalf of the project team following the contract between AIT and the Japan International Cooperation Agency (JICA) for the project period of March 2015 - December 2017. Technical support is provided by the Asia Center for Air Pollution Research (ACAP) Japan and the operational support is provided by the Pollution Control Department (PCD) of Thailand. The project aims at characterizing the particulate matter (PM) level and composition, ambient concentrations of acidic gases, as well as the ionic components of rainwater at two sites in the Bangkok Metropolitan Region (BMR): AIT (Pathumthani) and PCD (Bangkok). During the sampling period of September 2015 - February 2017, 78 weekly samples were collected for PM and acid gases (filter pack samplers) and rainwater (automatic wet-only collectors), respectively. The PM mass and ionic compositions were analyzed by AIT while the EC/OC were analyzed by ACAP. The sampling and analysis were done strictly following the required QA/QC procedure introduced by ACAP. The source apportionment study for PM2.5 measured at the sites was done using receptor models (the Chemical Mass Balance (CMB) Model and the Positive Matrix Factorization (PMF) Model). An emission inventory of PM and precursors was conducted for the BMR for the base year 2015 and the data were used to run a three-dimensional air quality modeling system of Weather Research Forecast – Comprehensive Air Quality Model with Extensions (WRF-CAMx) to simulate PM in BMR for August and November 2015. The simulation results were evaluated using the monitoring data.\nIn the dry period, the average fine (PM2.5) and coarse (PM>2.5) concentrations at AIT (32 ± 11 and 44 ± 18 μg/m3) were higher than PCD (28 ± 10 and 41 ± 15 μg/m3) while in the wet period, the levels at the two sites were close, i.e. 15 ± 11 μg/m3 and 37 ± 18 μg/m3 at AIT and 15 ± 6 μg/m3 and 38 ± 17 μg/m3 at PCD. At both sites, PM2.5 mass contributed more to the total suspended particulate matter (SPM = PM2.5 + PM>2.5) in the dry period, about 42-43%, than in the wet period (30-31%). The average EC and OC levels in PM2.5 measured at AIT (3.60 ± 2.19 μg/m3 and 5.52 ± 4.59 μg/m3, respectively) were higher than those at PCD (2.75 ± 1.44 μg/m3 and 4.29 ± 3.34 μg/m3, respectively). The EC and OC in the coarse fraction (PM>2.5) at AIT were 1.07 ± 0.57 μg/m3 and 2.40 ± 1.97 μg/m3, respectively, that were also higher than the corresponding levels measured at PCD, 0.84 ± 0.55 μg/m3 and 1.80 ± 0.67 μg/m3.\nAt both sites, the most dominant anion species in PM2.5 was SO42- in both periods, i.e. the average levels at AIT for the wet and dry period were 2.37 μg/m3 and 4.10 μg/m3, respectively, while the corresponding values at PCD were 2.49 μg/m3 and 3.22 μg/m3, respectively. NH4+ was the major cation in PM2.5 at both sites that contributed 1.55 μg/m3 and 0.78 μg/m3 at AIT, in wet and dry period, respectively, while corresponding levels at PCD were 0.79 μg/m3 and 1.41 μg/m3. The source apportionment (CMB) results showed that the major contributing sources to PM2.5 in both sites were traffic (diesel vehicles) and biomass open burning (OB) but their relative contributions varied with season. During the dry period higher relative contributions from biomass OB (38% at AIT and 35% at PCD) were obtained\n5\nas compared to the wet period (24.9% at AIT and 24.6% at PCD). The opposite was for the traffic contribution that was higher during the wet period (29% at AIT and 26% at PCD) than the dry period (27% at AIT and 21% at PCD) which may be explained by more intensive OB in BMR during the dry period. The full data set of PM2.5 compositions at the sites should be scrutinized to improve the source apportionment also by using the multivariate statistical model of PMF. Back trajectory (HYSPLIT) analyses showed that the weeks with high PM in BMR were normally characterized by the stagnant regional pathway of airmass while low PM period weeks were generally associated with the marine pathway of airmass.\nAverage pH of rainwater at AIT and PCD were 4.7 – 7.0 and 4.6 – 7.1, respectively, with the lower values recorded for the dry period and higher values were for the wet period. The average electrical conductivity of rainwater was 2.08 ± 1.65 mS/m for AIT and 2.02 ± 1.11 mS/m for PCD. The total annual wet deposition fluxes for different species at both sites ranged from 5.3 to 86.1 meq/m2 with the following rank: NH4+>Ca2+>NO3->SO42->Cl->Na+>K+>Mg2+. The concentrations of acidic gases measured at both sites ranged from 0.6 to 13.5 ppb following the rank of NH3 > SO2 > HNO3 > HCl. The dry deposition was calculated and the results were well below those of the wet deposition fluxes, especially during the rainy months. This implied that the wet deposition played an important role to remove sulfur (S) and nitrogen (N) species from the BMR atmosphere. The total sulfur deposition in 2016 was estimated at 586 kg/km2/yr while that of nitrogen was 2,235 kg/km2/yr which were still lower than the critical loads suggesting a low potential risk for the terrestrial ecosystem in Pathumthani at present.\nEmission inventory results showed that on-road transport contributed the most to the total emissions of NOx, CO, NMVOC, PM10, PM2.5, BC and OC (37 - 65%), while NH3 emission was mainly from livestock (55%) and SO2 was mainly from industry (90%). WRF simulation results were evaluated using the observations at two airports in BMR and the results showed satisfactory performance for temperature and relative humidity, but not for wind speed and wind direction. CAMx simulation results of PM2.5 showed higher concentrations in the city center for all months which also reflected the contributions from the traffic emissions. The CAMx could not capture the hourly PM2.5 recorded at three available PCD monitoring stations for both August and November. However, the comparison between CAMx simulated and weekly PM monitoring results obtained in this project showed more reasonable agreement.\nA better characterization of PM in BMR requires a long-term monitoring period. The findings suggest that the traffic and biomass OB are the key sources contributing to PM; however PM mass and composition data collected over a longer period would provide better source apportionment results by using more advanced receptor models, such as PMF.The model simulation for PM should be conducted for the entire year to capture the seasonal variation and modelling tools should be applied to assess impacts of emission reduction scenarios on air quality and health as well as the co-benefit to the climate forcing reduction. The results of this project provide the scientific evidence to policy making toward better air quality in BMR.","subitem_description_type":"Abstract"}]},"item_10008_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.18884/00000837","subitem_identifier_reg_type":"JaLC"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kim Oanh, Nguyen Thi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1098","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2018-03-09"}],"displaytype":"detail","filename":"UrbanairpollutionFinal_report.pdf","filesize":[{"value":"4.7 MB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"UrbanairpollutionFinal_report","url":"https://jicari.repo.nii.ac.jp/record/845/files/UrbanairpollutionFinal_report.pdf"},"version_id":"7633cb8d-9204-485e-8c04-2b3c83c64b10"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"book","resourceuri":"http://purl.org/coar/resource_type/c_2f33"}]},"item_title":"Research project on “A Study in Urban Air Pollution Improvement in Asia”","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Research project on “A Study in Urban Air Pollution Improvement in Asia”"}]},"item_type_id":"10008","owner":"18","path":["602","627"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-09"},"publish_date":"2018-03-09","publish_status":"0","recid":"845","relation_version_is_last":true,"title":["Research project on “A Study in Urban Air Pollution Improvement in Asia”"],"weko_creator_id":"18","weko_shared_id":-1},"updated":"2023-05-15T13:20:52.720450+00:00"}