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The determination of background concentration of PM 2.5 is important to understand the contribution of local emission sources to total PM 2.5 concentration. The purpose of this study was to exam the performance of baseline separation techniques to estimate PM 2.5 background concentration. Five separation methods, which included recursive digital filters (Lyne-Hollick, one-parameter algorithm, and Boughton two-parameter algorithm), sliding interval and smoothed minima, were applied to one-year PM 2.5 time-series data in two heavily polluted cities, Tianjin and Jinan. To obtain the proper filter parameters and recession constants for the separation techniques, we conducted regression analysis at a background site during the emission reduction period enforced by the Government for the 2014 Asia-Pacific Economic Cooperation (APEC) meeting in Beijing. Background concentrations in Tianjin and Jinan were then estimated by applying the determined filter parameters and recession constants. The chemical mass balance (CMB) model was also applied to ascertain the effectiveness of the new approach. Our results showed that the contribution of background PM concentration to ambient pollution was at a comparable level to the contribution obtained from the previous study. The best performance was achieved using the Boughton two-parameter algorithm. The background concentrations were estimated at (27 ± 2) μg/m 3 for the whole year, (34 ± 4) μg/m 3 for the heating period (winter), (21 ± 2) μg/m 3 for the non-heating period (summer), and (25 ± 2) μg/m 3 for the sandstorm period in Tianjin. The corresponding values in Jinan were (30 ± 3) μg/m 3 , (40 ± 4) μg/m 3 , (24 ± 5) μg/m 3 , and (26 ± 2) μg/m 3 , respectively. The study revealed that these baseline separation techniques are valid for estimating levels of PM 2.5 air pollution, and that our proposed method has great potential for estimating the background level of other air pollutants. [ABSTRACT FROM AUTHOR]

China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O 3 showed significant declines from 2013 to 2020, with PM 2.5 annual decline rates of 6–9% in most cities. In contrast, O 3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O 3 to NO 2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O 3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O 3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. [Display omitted] • We used a random forest method to remove meteorological effect for air quality data. • The deweathered results show smoothed but similar variation in annual trends. • The meteorology can play positive or negative role depending on specific conditions. • Beijing shows positive meteorology in reducing pollutants for two clean air actions. [ABSTRACT FROM AUTHOR]

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate. [ABSTRACT FROM AUTHOR]

The effect of human activities on the characteristics and variations of indoor air pollutants is poorly understood, in particular for the classroom environment due to the limited reports at present. In this study, an observational campaign (17 days) was carried out in two selected classrooms (one equipped with air purifier) in a primary school of Hangzhou, China. Highly time-resolved particulate matter (PM) and black carbon (BC) were measured using Aerodynamic Particle Sizer (APS) and Aethalometer, and the diurnal variations of PM 2.5 , PM 10 , PM 20 and BC were characterized in detail. It was found that the student activities occurred throughout the daytime and could result in rapid changes in air pollutants. For example, the daytime student activities (e.g., chasing and running) strongly enhanced the PM level and changed PM diurnal pattern, in particular for coarse particles (2.5–10 μm) with short-term spikes at intervals, which was not observed under vacant conditions. The indoor BC did not show clear diurnal patterns, regardless of working days and weekends. Through absorption exponent analysis, it was inferred that the source of indoor BC was stable at most of the time, and possibly related to traffic emission. Besides, three volatile organic compounds (VOCs) samples were collected from the two classrooms, and the laboratory analysis results showed that oxygenated VOCs was a major contributor to indoor VOCs. Through inter-comparison analysis of indoor and outdoor samples, it was further found that the VOC profile of classrooms has a similar pattern with outdoor environments. Finally, the most abundant VOC species of classroom environment were identified, and their potential sources were estimated. • Variations and characteristics of PM, BC and VOCs were characterized in classroom environment. • Student activities were identified as a major source of classroom PM under occupied conditions. • The source of indoor BC was stable at most of the time. • The VOC profile of classroom environment showed similar pattern with outdoor air. [ABSTRACT FROM AUTHOR]