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The atmospheric PM2.5, PM2.5/PM10, PCDD/Fs-WHO2005-TEQ, and PCDD/F (polychlorinated dibenzo-p-dioxins and dibenzofuran) phase distributions of 23 cities in southern China, during 2014-2016, were investigated in this study. In general, the cities with higher latitudes had higher PM2.5concentrations than those with lower latitudes. During 2014- 2016, the lowest three-year average concentrations of PM2.5occurred at Sanya and Haikou and were 16.4 and 21.7 μg m−3, respectively; while the highest concentrations of PM2.5was occurred at Wuhan and Luzhou and were 68.8 and 63.1 μg m−3, respectively. During 2015–2016, the PM2.5concentrations of most of cities decreased, but those of five cities (Chengdu, Luzhou, Nanchang, Qujing and Quanzhou) increased, indicting that the air quality of these five cities was still not well controlled. The average RMvalues of the 23 cities were 5.20, 4.49 and 4.13 in 2014, 2015 and 2016, respectively, which revealed that the PM2.5concentrations in the cities of southern China slowly decreased, although they were still far above the WHO air quality regulated standard (10 μg m−3). In general, a city with a higher PM2.5concentration was also had a higher PM2.5/PM10ratio. Among the 23 cities, the six highest three-year averages of total-PCDD/Fs-WHO2005-TEQ concentrations were 0.0665, 0.0633, 0.0625, 0.0600, 0.0528 and 0.0526 pg-WHO2005-TEQ m−3in Chengdu, Wuhan, Nanjing, Hefei, Luzhou and Hangzhou, respectively. During 2014, the six cities (Hefei, Nanjing, Wuhan, Guiyang, Shanghai and Chengdu) with the lowest temperatures in winter (an average of 5.4°C), their average particle phase fractions of total-PCDD/Fs-WHO2005-TEQ that were approximately 76%, 53%, 71% and 93% in the spring, summer, fall and winter, respectively; while, the six cities (Haikou, Fuzhou, Guangzhou, Nanning, Nanchang and Changsha) with the highest temperatures in summer (an average of 16.5°C), had average particle phase fractions of total-PCDD/Fs-WHO2005-TEQ that were approximately 61%, 42%, 57% and 81% in the spring, summer, fall and winter, respectively. The results of this study provide information showing the trends of both atmospheric PM2.5and PCDD/Fs in the cities of southern China. In addition, this study provided the overview relating to the PM2.5and PCDD/Fs in ambient air of southern China, which was not reported in previous studies. The results of this study were of great importance to present the trends of air quality in China. It is also useful for the establishment of control strategies in the future.

In this study 15 fly ash samples were collected from 15 large-scale coal-fired power plants (CFPPs) in China. The samples were then re-suspended through PM2.5and PM10inlets and analyzed for the contents of 39 inorganic elements (IEs) using inductively coupled plasma-mass spectroscopy (ICP-MS) and inductively coupled plasma-optical emission spectrometry (ICP-OES). The results show that the particle size distributions for the 15 FAs samples exhibited bimodal patterns. The Σ39IEs (g g−1) for the PM2.5(0.292-0.564) in all the 15 CFPPs were higher than that of PM10(0.269-0.403). Except for Cu, all the other 38 IEs were more enriched in the PM2.5with the PM2.5/PM10ratios being 1.06-1.73. Considering 13 heavy metals, the same orders occurred between PM2.5and PM10with Al ≫ Cr > Zn > Mn > Cu > V > Pb > Sn > Co > As > Sb > Tl > Cd. More attention should be paid to the high contents of Cr in both PM2.5(1310 mg g−1) and for PM10(1240 mg g−1) from all 15 CFPPs. 23 IEs for PM10and 26 IEs for PM2.5had the geo-accumulation index (Igeo) values higher than 0, indicating different pollution levels for them. On the other hand, there was moderate to extreme levels of pollution for Cr, Zn, Cu, Pb, Sn, Sb, Tl, Cd and Al based on Igeo values. The element profiles for PM2.5or PM10from 15 CFPPs were similar based on low coefficients of divergence for PM2.5(0.254 ± 0.038) and PM10(0.244 ± 0.054) according to the comparison between any two CFPPs. Most elements with low relative enrichment factors (REF) as less than 0.70 or 0.70-1.30 indicated no or weak condensation occurred for them during coal combustion, while Cr, Cu, Zn, Sn, W and Pb had REF values higher than 1.30 indicated that significant condensation occurred for these elements.

In order to analyze the characteristics and long-term trend levels of PM2.5in Tainan City, Taiwan, Mesoscale Modeling System Generation 5 (MM5) and Community Multiscale Air Quality Model (MM5-CMAQ) modelling as well as box plots and time series analysis, were utilized in this study. The long-term trend analysis shows that the levels of PM2.5(averaged at 38.3 µg m−3and ranged between 33.1 and 41.9 µg m−3) in Tainan City for ten years (2005–2014) were above the yearly average standards of 15 µg m−3, showing non-attainment status. Overall, the results show a decreasing trend (from 41.9 µg m−3in 2005 to 35.0 µg m−3in 2014) in the levels of PM2.5in Tainan atmosphere in the ten year period. The results of the MM5-CMAQ air quality modeling, indicate that the highest contribution on PM2.5in Tainan City was from trans-boundary pollution from neighbouring cities (34.2%), while long-range transport and local emissions from Tainan each contributed a fraction of approximately 32.9%. In terms of local sources, the highest influence is from area sources (18.6%), followed by line sources (7.7%) and point sources (6.6%). Thus, to control PM2.5in Tainan City, the focus should be on construction, road dust, and residential activities.

Fine particulate matters (PM2.5) have been identified as one of the major air pollutants in urban areas, which are responsible for the deterioration of the atmospheric air quality as well as adverse effects on public health. In this study, the mass concentration, water-soluble ionic component, trace metal component, carbon component and modeling the contribution source for PM2.5was characterized for Chiayi City, which has high population density and surrounded by agricultural area. The lowest PM2.5mass concentrations were registered in the summer (9–22 µg m−3), while for the spring, autumn, and winter were well above the healthy level suggested by World Health Organization (WHO). For all seasons, the dominants were the sulfate (SO42−), nitrate (NO3−) and followed closely by ammonium (NH4+). Those secondary aerosols were transformed from SO2and NO2into particulate NO3−and SO42−during spring, autumn and winter. Lower carbon mass concentrations were observed for summer (2.03–2.49 µg m−3) corresponding to the highest carbon content in PM2.5mass concentrations in terms of percentages (average 18.1%). Using the Chemical Mass Balance receptor model, the secondary nitrate (NO3−), primary traffic source, secondary sulfate (SO42−), re-suspending soil particle, and petrochemical industry were identified as the major sources of PM2.5in Chiayi City. Consequently, the PM2.5contributions were complicated in a small but various seasons and geological distributing area. The air quality control strategies were thus seasonal and periodical dependent.

Kaohsiung County, in southern Taiwan, has both highly industrial and rural areas. In this study, the characteristics of PCDD/Fs in the ambient air of Kaohsiung (urban), Meinong (rural) and Xiaogang (heavy industrial zone) in 2014 and 2015 were modeled based on the prevailing meteorological conditions and the measured ambient air concentrations of PM2.5, PM10and TSP. The yearly average PM2.5concentrations in the ambient air of the three areas were in the range of 23 to 31 µg m–3, all above the National Air Quality Standard of Taiwan (15 µg m–3). The simulated average concentrations of PCDD/Fs in the whole of Kaohsiung area in terms of toxicity equivalent were in the range of 0.034–0.053 pg WHO2005-TEQ m–3. The average total deposition fluxes of total-PCDD/Fs-WHO2005-TEQ ranged between 155.4 and 276.6 pg WHO2005-TEQ m–2month–1with 1,2,3,7,8-PeCCD and 2,3,4,7,8-PeCDF being the dominant congeners in terms of PCDD/Fs WHO2005-TEQ. Xiaogang area with highly industrial activities had the highest concentrations of PM2.5and PCDD/Fs and corresponding total-PCDD/Fs WHO2005-TEQ deposition fluxes, while the Meinong in the rural site recorded the lowest. Average dry deposition velocities of total PCDD/Fs WHO2005-TEQ for both 2014 and 2015 were 0.162, 0.148 and 0.161 cm s–1for Kaohsiung, Meinong and Xiaogang, respectively, while the average scavenging ratios of total PCDD/Fs WHO2005-TEQ were 6232, 4701 and 6802, for Kaohsiung, Meinong and Xiaogang, respectively. The information provided in this work is useful for both further studies and environmental control strategies concerning atmospheric aerosols and dioxins.

Fine particulate matters (PM2.5) has been identified as one of the major air pollutants in urban areas, which are responsible for the adverse effects on public health and the deterioration of visibility. New PM2.5air quality standards were promulgated in Taiwan on 14thMay 2012, as well as the standard sampling and analytical method for atmospheric PM2.5(NIEA A205.11C) on 24thApril 2012. In this study, the atmospheric levels and characteristics of PM2.5in Tainan during 2013 were evaluated by measuring the mass concentration of PM2.5and analyzing the water-soluble ionic, carbon, and metal components. Additionally, a Chemical Mass Balance (CMB) receptor model was used to identify possible sources of PM2.5and their contributions. Based on results of this study, the current PM2.5levels in Tainan in spring and winter (41–49 µg m-3) were substantially higher than the yearly average PM2.5air quality standards (15 µg m-3). According to chemical composition analysis, secondary aerosols (NH4+, NO3-, and SO42-) contributed approximately 50% and 60% of PM2.5mass in spring and winter respectively; but were responsible about 40% by mass in summer at both Tainan and Xinying stations. From the results of CMB model, the main contribution sources to the PM2.5in Tainan are traffic emissions (31.5%), ammonium sulfate (25.5%), ammonium nitrate (12.5%), and crustal elements (11%). Consequently, to improve PM2.5of Tainan City, the priority control pollutants (or sources) are primary PM2.5(open burning, construction sites and road dust by vehicles), NOx(diesel vehicle emissions), and SOx(fuels).