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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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of COVID-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in terms of air quality which is shown to have improved for a number of pollutants, such as Nitrogen Dioxides (NO 2 ) and Carbon Monoxide (CO), based on monitoring data on the ground and from a satellite. In addition to primary pollutants CO and NO x emitted from mobile sources, PM 2.5 (primary and secondary) and secondary Ozone (O 3 ) during the lockdown period will also be analyzed using both statistical methods on air quality data and the modelling method with emission and meteorological data input to an air quality model. By estimating the decrease in traffic volume in the Sydney region, the corresponding decrease in emission input to the Weather Research and Forecasting-Community Multiscale Air Quality Modelling System (WRF-CMAQ) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO 2 , O 3 , and PM 2.5 in the Greater Metropolitan Region (GMR) of Sydney. The results from both statistical and modelling methods show that NO 2 , CO, and PM 2.5 levels decreased during the lockdown, but O 3 instead increased. However, the change in the concentration levels are small considering the large reduction of ~30% in traffic volume.

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM 2.5 having the correlation (R 2 ) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census district results over NSW, we estimated that there were 247 (CI: 89, 409) premature deaths, 437 (CI: 81, 984) cardiovascular diseases hospitalizations and 1535 (CI: 493, 2087) respiratory diseases hospitalizations in NSW over the period from 1 November 2019 to 8 January 2020. The results are comparable with a previous study based only on observation data, but the results in this study provide much more spatially and temporally detailed data with regard to the health impact from the summer 2019-2020 wildfires.

Haze formation involves many interacting factors, such as secondary aerosol formation, unfavourable synoptic conditions and regional transport. The interaction between these factors complicates scientific understanding of the mechanism behind haze formation. In this study, we investigated the factors resulting in haze events in Longyou, a city located in a basin in China. Aerosol samples of PM 2.5 were collected for subsequent chemical composition analysis between 11 January and 5 February 2018. The impacts of wind on PM 2.5 , SO 2 and NO 2 concentrations were analysed. Besides, the origin of air parcels and potential sources of PM 2.5 were analysed by backward trajectory, potential source contribution function (PSCF) and concentration-weighted trajectories (CWT). Among the water-soluble ions identified, NO 3 - had the highest concentration, with further analysis demonstrating the haze evolution was mainly driven by the reactions involving NO 3 - formation. The dramatic increase of nitrate is mainly due to the homogeneous reaction of nitric acid with ammonia, while sulfate is likely due to heterogeneous reactions of NO 2 , SO 2 and NH 3 . The average wind speed was less than 2 m/s during the aerosol sampling period, which could be considered as a stagnant state. Pollutants emitted by industrial area located in the northeast Longyou were probably brought to observation sites by continuous wind from northeast and accumulated gradually. Air parcels originating from the northeast of Zhejiang province also had large effects on haze pollution in Longyou. Together, our results showed that rapid secondary aerosol formation and unfavourable synoptic conditions are the main factors resulting in haze pollution in Longyou., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Environmental monitoring and modelling, especially in the regional context, has seen significant progress with the widely usage of satellite measurement in conjunction with local meteorological and air quality monitoring to understand the atmospheric dispersion and transport of air pollutants. This paper studies the application of these data and modelling tools to understand the environment effects of a major bushfire period in the state of New South Wales (NSW), Australia, in 2013. The bushfires have caused high pollution episodes at many sites in the greater Sydney metropolitan areas. The potential long-range transport of aerosols produced by bushfires to other region and states has been seen by regulators as a major concern. Using data and images collected from satellites, in addition to the results obtained from different simulations carried out using HYSPLIT trajectory model and a regional meteorological model called Conformal Cubic Atmospheric Model (CCAM), we were able to identify at least 2 days on which the smoke aerosols from bush fires in NSW has been transported at high altitude to the northern state of Queensland and the Coral Sea. As a result, widespread high particle concentration in South East Queensland including the Brisbane area, as measured by nearly all the air quality monitoring stations in this region, occurred on the day when the smoke aerosols intruded to lower altitude as indicated by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Lidar measurements on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The use of meteorological or air quality modelling to connect the ground-based measurements with satellite observations as shown in this study is useful to understand the pollutant transport due to bushfires and its impact on regional air quality.

During the 2016 Hangzhou G20 Summit, the chemical composition of submicron particles (PM 1 ) was measured by a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) along with a suite of collocated instruments. The campaign was undertaken between August 5 and September 23, 2016. The impacts of emission controls and meteorological conditions on PM 1 chemical composition, diurnal cycles, organic aerosol (OA) source apportionment, size distribution and elemental ratios were characterized in detail. Excluding rainy days, the mean PM 1 mass concentration during G20 was 30.3 μg/m 3 , similar to that observed before G20 (28.6 μg/m 3 ), but much lower than that after G20 (42.7 μg/m 3 ). The aerosol chemistry during the three periods was substantially different. Before G20, high PM 1 loading mostly occurred at daytime, with OA accounting for 60.1% of PM 1 , followed by sulfate (15.6%) and ammonium (9.1%). During G20, the OA fraction decreased from 60.1% to 44.6%, whereas secondary inorganic aerosol (SIA) increased from 31.8% to 49.5%. After G20, SIA dominated high PM 1 loading, especially at nighttime. Further analysis showed that the nighttime regional transport might play an unfavorable role in the slight increase of secondary PM 1 during G20, while the strict emissions controls were implemented. The OA (O/C = 0.58) during G20 was more aged, 48.7% and 13.7% higher than that before and after G20 respectively. Our study highlighted that the emission controls during G20 were of great success in lowering locally produced aerosol and pollutants, despite of co-existence of nighttime regional transport containing aerosol high in low-volatile organics and sulfate. It was implied that not only are emissions controls on both local and regional scale important, but that the transport of pollutants needs to be sufficiently well accounted for, to ensure the successful implementation of air pollution mitigation campaigns in China., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Modelling and forecasting of air pollution from bushfires or hazardous reduction burnings is important in providing information and allowing measures to be taken to reduce the exposure of people from harmful effect of air pollutants from fire events. In this work, the meteorological, chemical transport model and air quality models developed by the Commonwealth Scientific and Industrial Research Organisation of Australia are used in conjunction with the smoke emission model to simulate the formation and dispersion of particle aerosols (PM2.5) in the Greater Metropolitan Region of Sydney as the results of emission of different pollutant species from hazardous reduction burnings or from bushfires. The smoke emission model is based on a fire model describing two distinct fire behaviours: flaming and smothering. There are several schemes that can be used to estimate the emission factors of different emitted species from fire of various vegetation types. A comparison of these schemes is performed by comparing the air quality model output from air quality model with observation from monitoring stations in a case study, the May 2016 prescribed burning winter period, which caused elevated particle concentrations in the Sydney basin. The PM2.5 prediction over the Greater Metropolitan Region of Sydney from the forecasting modelling tool will then be used to calculate the population exposure and health impacts due to the May 2016 fire event as a case study. The three main health endpoints considered for health impacts are mortality, respiratory-related and cardiovascular hospitalisations. The results are comparable with other studies using non-modelling methods of determining the exposure and health impacts on this event. [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]