Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), International Joint Laboratory on Climate and Environment Change (ILCEC), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China.
Department of Physics, School of Sciences and Humanities, Koneru Lakshmaiah Education Foundation, K. L. University, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India.
Environ Sci Pollut Res Int. 2019 Dec;26(36):36568-36588. doi: 10.1007/s11356-019-06730-6. Epub 2019 Nov 15.
Monitoring of particulate matter (PM) is important in air quality, public health, and epidemiological studies and in decision-making for policy implementation. In the present study, the temporal variability of surface-measured PM concentrations ([PM]) and their relationship with meteorological variables and aerosol optical depth (AOD), with the aid from source apportionment studies, are investigated at four urban cities in the Chinese Yangtze River Delta (YRD) region during January 2014 to December 2017. The annual mean concentrations of [PM] ([PM]) observed at Shanghai (SH), Nanjing (NJ), Hangzhou (HZ), and Hefei (HF) were 46.98 ± 12.21, 54.84 ± 46.14, 52.82 ± 16.98, and 64.03 ± 20.57 μg m (68.07 ± 14.33, 96.48 ± 26.86, 83.08 ± 22.38, and 97.61 ± 20.19 μg m), respectively. However, the [PM] exceeded the Chinese National Air Quality Standards of GB3095-2012, being higher (lower) during winter (summer). The [PM] was found higher in the morning (08:00-10:00 LT) and evening (18:00-20:00 LT) and lower in early morning (04:00 LT) and afternoon (14:00 LT) attributed to the dynamics of boundary layer height and varied emission sources. With an annual mean of 0.6-0.7, the PM ratio (PMr = PM/PM) was observed to have a single peak distribution in all seasons indicating the dominance of fine particles (PM). Further, the [PM] and [PM] were highly correlated (r ≥ 0.90) in all cities, with slope > 0.70 representing the abundance of fine particles, except for NJ (< 0.70). A low correlation (< 0.5) was noticed between [PM] and AOD suggesting that the aerosol particles had a large influence on AOD, contributing less to PM. Finally, the concentration bivariate probability function (CBPF) and trajectory statistical models like potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) suggested that local and regional sources contributed a lot for the high [PM] observed at the four cities in the YRD, China.
在中国长江三角洲（YRD）地区的四个城市上海（SH）、南京（NJ）、杭州（HZ）和合肥（HF），于 2014 年 1 月至 2017 年 12 月期间进行了一项研究，以监测大气中颗粒物（PM）的浓度变化，并分析其与气象变量和气溶胶光学深度（AOD）的关系。本研究使用源分配研究来帮助分析，结果表明，在这四个城市中，颗粒物的表面浓度（[PM]）存在季节性变化。研究发现，上海、南京、杭州和合肥的年均[PM]浓度分别为 46.98±12.21μg/m（68.07±14.33μg/m）、54.84±46.14μg/m（96.48±26.86μg/m）、52.82±16.98μg/m（83.08±22.38μg/m）和 64.03±20.57μg/m（97.61±20.19μg/m）。然而，这些城市的[PM]浓度均超过了中国国家空气质量标准（GB3095-2012），冬季浓度较高（夏季浓度较低）。此外，[PM]浓度在早晨（08:00-10:00LT）和傍晚（18:00-20:00LT）较高，在清晨（04:00LT）和下午（14:00LT）较低，这归因于边界层高度和不同排放源的动态变化。在所有季节中，PM 比（PMr=PM/PM）的平均值为 0.6-0.7，呈现出单峰分布，这表明细颗粒物（PM）占主导地位。此外，在所有城市中，[PM]和[PM]之间都高度相关（r≥0.90），斜率>0.70 代表细颗粒物的丰度，除了 NJ（<0.70）。[PM]与 AOD 之间的相关性较低（<0.5），这表明气溶胶粒子对 AOD 有很大的影响，但对 PM 的贡献较小。最后，浓度双变量概率函数（CBPF）和轨迹统计模型（如潜在源贡献函数（PSCF）和浓度加权轨迹（CWT））表明，本地和区域来源对长三角地区四个城市的高[PM]浓度有很大影响。
