Abstract:In this study, the spatial patterns and their interannual variability of wintertime low-level winds over the Indochina Peninsula (IDP) were studied by using the analysis of the empirical orthogonal function for complex numbers. The leading mode accounts for 46.6% of the total variance. The composite and regressed patterns of wind components show dominant northeasterly wind over the IDP, which are related to the East Asia winter monsoon (EAWM) circulation and connected to the cyclonic circulation near Borneo. The correlations between the EAWM indices and the leading principal component (PC) suggest the plausible connections between the low-level wind over the IDP and EAWM predominantly via the wind circulation. We also performed correlation analysis on the relationship between leading mode and sea surface temperature anomalies (SSTAs). The result indicates that there is a linkage between the northeasterly wind over the IDP and EAWM and with SSTAs in the Pacific Ocean. This study provides useful information and a mechanism related to the monsoon variability over the IDP. OPEN ACCESSAtmosphere 2014, 5 102

Particulate matter (PM) less than 2.5 micron (PM2.5) issue is 1 of the important targets of concern by the United Nations’ Sustainable Development Goals. Bangkok is a megacity and facing air pollution problems. This study analyzed PM, PM2.5 and PM less than 10 micron (PM10), monitoring data from stations located in Bangkok, and aimed to present their variations in diurnal, weekly, and intra-annual timescales. High PM concentrations are related to calm wind. The diurnal variation of PM2.5/PM10 suggested a greater accumulation of PM2.5 than PMcoarse during the low wind speed. Potential source areas affecting PM rising at each monitoring station were identified using statistical technique, bivariate polar plot, and conditional bivariate probability function. Results showed that Ratchathewi District Monitoring Station identified 3 potential source areas related to emissions from transportation sources creating rising PM concentrations. The first potential source was located in the northwest direction, namely, the Rama VI Road close to the conjunction with Ratchawithi Road. The second potential source area was located around the cross-section between Phaya Thai Road and Rama I Road, while the third was located at the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road. These potential source areas constitute useful information for managing and reducing PM.

Na Phra Lan Subdistrict is a pollution control zone with the highest PM10 level in Thailand. Major mobile and industrial sources in the area are related to stone crushing, quarrying and mining. This study used statistical techniques to investigate the potential sources influencing high PM10 levels in Na Phra Lan. Hourly PM10 data and related parameters (PM2.5, PMcoarse and NOx) from 2014–2017 were analysed using time series, bivariate polar plot and conditional bivariate probability function (CBPF). Results of diurnal variation revealed two peaks of PM10 levels from 06:00–10:00 and 19:00–23:00 every month. For seasonal variation, high PM10 concentrations were found from October to February associated with the cool and dry weather during these months. The bivariate polar plot and CBPF confirmed two potential sources, i.e., resuspended dust from mobile sources close to the air quality monitoring station (receptor) and industrial sources of mining, quarrying and stone crushing far from the station on the northeast side. While the industrial source areas played a role in background PM10 concentrations, the influence of mobile sources increased the concentrations resulting in two PM10 peaks daily. From the study results, we proposed that countermeasure activities should focus on potential source areas, resuspended road dust from vehicles and the industrial sources related to quarrying and mining, rather than distributing equal attention to all sources.

Monitoring of ambient air quality yields data typically presented as time series plots, tables of summarized statistical values, or other representations. This paper presents an alternative way to visualizing air quality monitoring data by presenting concentrations in the form of a calendar, offering a familiar way for reader to identify air quality trends on various time scales(daily, weekly, or monthly). One of the major air pollution problems in the northern part of Thailand is haze, which is related tothe concentration of airborne particulates less than 10 microns in size (PM10). This paper presents calendars of PM10 concentrations monitored by the Pollution Control Department across northern Thailand. Hourly mean PM10 concen-trationsmonitored at 13 stations were used to construct PM10 concentration calendars for each station. Haze episodes are clearly identifiable in the visualization; the calendar also allows easy comparison of PM10 levels between years. We also observed the absenceof any haze episodes in 2011, and propose possible related factors.