Atmospheric Environment

An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Jia Xu, Wen Yang, Bin Han, Meng Wang, Zhanshan Wang, Zhiping Zhao, Zhipeng Bai, Sverre Vedal Abstract Modeling fine-scale spatial and temporal patterns of air pollutants can be challenging. Advanced spatio-temporal modeling methods were used to predict both long-term and short-term concentrations of six criteria air pollutants (particulate matter with aerodynamic diameter less than or equal to 10 and 2.5 μm [PM10 and PM2.5], SO2, NO2, ozone and carbon monoxide [CO]) in Beijing, China. Monitoring data for the six criteria pollutants from April 2014 through December 2017 were obtained from 23 administrative monitoring sites in Beijing. The dimensions of a large array of geographic covariates were reduced using partial least squares (PLS) regression. A land use regression (LUR) model in a universal kriging framework was used to estimate pollutant concentrations over space and time. Prediction ability of the models was determined using leave-one-out cross-validation (LOOCV). Prediction accuracy of the spatio-temporal two-week averages was excellent for all of the pollutants, with LOOCV mean squared error-based R2 (R2mse) of 0.86, 0.95, 0.90, 0.82, 0.94 and 0.95 for PM10, PM2.5, SO2, NO2, ozone and CO, respectively. These models find ready application in making fine-scale exposure predictions for members of cohort health studies and may reduce exposure measurement error relative to other modeling approaches. Graphical abstract

Application of a sensor network of low cost optical particle counters for assessing the impact of quarry emissions on its vicinity Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Yuval, Hadas Magen Molho, Ohad Zivan, David M. Broday, Raanan Raz Abstract The adverse health effects of inhaled particulate matter (PM) are global concern. Yet, in general, estimating the exposure to PM is challenging due to the sparsity of standard air quality monitoring stations and the low accuracy of dispersion models when high spatial resolution is required. The city of Elad, Israel, is situated less than 1 km from the Migdal Tzedek stone quarry, and public concerns were raised regarding the impact of the quarry's PM emissions on the city air quality. This work describes a year-long campaign of continuous measurements of particle number concentration (PNC) in few locations in the city and its vicinity, using a network of low cost optical particle counters (OPCs). To assess the possible impact of the quarry on the city we examined the OPCs' accuracy, coherency, and their capability to detect the quarry's impact on the PNC levels in the city. Using PNC time series in two size channels from a network of five nodes, PM10 and PM2.5 records from a nearby reference air quality monitoring station, and meteorological data, we could conclude that the quarry's impact on the city was small relative to the background PNC levels. Yet, more importantly, this work demonstrates the use of a network of low cost OPCs for responding to an environmental query for which the sparse standard air quality monitoring observations were not sufficient. The trade-offs between deployment of a network of cheap low-quality instruments and the use of a single high-end device are discussed. Graphical abstract

Establishment of county-level emission inventory for industrial NMVOCs in China and spatial-temporal characteristics for 2010–2016 Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Maimaiti Simayi, Yufang Hao, Jing Li, Rongrong Wu, Yuqi Shi, Ziyan Xi, Yang Zhou, Shaodong Xie Abstract Volatile organic compounds (VOCs) pollution, which is closely linked to photochemical smog and secondary organic aerosols, has become a severe concern in China. Therefore, we compiled a new high-resolution emission inventory for the industrial non-methane Volatile organic compounds (NMVOCs) using "bottom-up" approaches throughout 2010 and 2016. In this work, the industrial sources were divided into five major categories, and 108 specific sources, as well as an emission factor database, was developed for industrial NMVOCs. Results indicated that the total NMVOCs emissions from industrial sources increased from 16.88 Tg in 2010 to 21.04 Tg in 2016 at an annual average rate of 3.7%. The five major source categories including "production of VOCs", "storage and transportation", "industrial processes using VOCs as raw material", "processes using VOCs-containing products", and "fossil fuel combustion" generated 1.92 Tg, 0.94 Tg, 6.54 Tg, 10.04 Tg, and 1.60 Tg NMVOCs, respectively, in 2016. Coke production, plastic manufacturing, raw medicine industry, and architectural decoration were the primary sources of industrial NMVOCs and emissions of these sources increased by 140 Gg, 190 Gg, 640 Gg, and 700 Gg between 2010 and 2016. The emissions displayed distinct spatial characteristics, with significantly higher emissions in the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Cheng-Yu region than in other areas. Shandong, Guangdong, Jiangsu, Zhejiang, and Henan were the top five provinces with the highest NMVOCs emissions, while the emission hotspots in the county-level were mainly distributed in Guangzhou urban area, Shanghai Pudong New Area, Hangzhou urban area, and Shenzhen urban area. The emissions in Henan province, Hubei province, and Cheng-Yu region increased significantly during the study period. Instead, emissions in some counties of Zhejiang province and Hebei province decreased than in 2010. Graphical abstract

Use of radar rainfall to model deposition of radionuclides Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Susan J. Leadbetter Abstract Accurate precipitation information is necessary for modelling the deposition of radionuclides following an accidental release of radioactive material to the atmosphere. The increase in resolution of numerical weather prediction models (NWP) has led to precipitation which looks more realistic but performs poorly when compared to observations using traditional grid-point statistics. Dispersion models have the ability to use precipitation information derived from radars. This study looks at the differences in estimates of air activity and deposition when precipitation information from a high resolution NWP is replaced by precipitation information from radar. Using a hypothetical release and a year's worth of meteorological data the results show that in around a third of cases there is a significant difference in the amount of deposition estimated when NWP precipitation is used compared to cases where radar precipitation is used. The results also show a small number of cases where air activity concentrations vary significantly between the runs.

Heterogeneous atmospheric degradation of current-use pesticides by nitrate radicals Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Coraline Mattei, Henri Wortham, Etienne Quivet Abstract In the atmosphere, pesticides are distributed between gaseous and particulate phases according to their physicochemical properties. In these two phases, they can react with atmospheric oxidants such as ozone, hydroxyl radical and nitrate radicals. Heterogeneous kinetics of the degradation by nighttime nitrate radicals are not well described. In this study, the heterogeneous reactivity with nitrate radicals of eight current-use pesticides (i.e., difenoconazole, tetraconazole, cyprodinil, fipronil, oxadiazon, pendimethalin, deltamethrin, and permethrin) adsorbed on silica model particles was investigated using laboratory experiments with in-situ nitrate radicals generation and concentration measurement. Under these experimental conditions, all pesticides were degraded. Atmospheric half-lives calculated with a Langmuir-Rideal model ranged between 8 days and 16 days and between 2 days and 11 days according to a Langmuir-Hinshelwood model for an atmospheric nitrate radicals concentration of 20 ppt. Results obtained can contribute to a better understanding of the atmospheric fate of pesticides in the particulate phase and show the importance of their degradation by nitrate radical compared to their degradation by other oxidants such as ozone and hydroxyl radicals. Graphical abstract

Fine and ultrafine particles concentrations in vape shops Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Charlene Nguyen, Liqiao Li, Chanbopha Amy Sen, Emilio Ronquillo, Yifang Zhu Abstract Vape shops are widespread due to the popularity of electronic cigarettes (e-cigs) as an alternative to tobacco cigarettes. In this study, sixty-seven Southern California vape shops were randomly surveyed for building characteristics, ventilation, and business patterns. Based on the survey results, six representative shops were recruited for real-time measurements of indoor and outdoor fine and ultrafine particles concentrations on a busy and less busy day. Occupancy, vaping frequency, and opening and closing of doors were recorded, and shop air exchange rate was determined. Indoor CO2, relative humidity, and temperature were also recorded. In addition, simultaneous measurements were taken at increasing distances away from a vaping area to assess the mixing and spatial profiles of particle levels inside the shops. During active vaping, real-time indoor particle number concentration and gravimetric-corrected PM2.5 mass concentration across the six vape shops varied from 1.3 × 104 to 4.8 × 105 particles/cm3 and from 15.5 to 37,500 μg/m3, respectively. The spatial profiles of particle number and mass were more uniformly mixed than expected in an indoor environment. Total vaping frequency was the main predictor of particle concentrations inside the vape shops when indoor-outdoor particle mass transfer is minimal (doors closed). Graphical abstract

An evaluation of the air quality health index program on respiratory diseases in Hong Kong: An interrupted time series analysis Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Tonya G. Mason, C. Mary Schooling, King Pan Chan, Linwei Tian Abstract Background On December 30th, 2013, the Hong Kong government implemented the Air Quality Health Index (AQHI) alert programme warnings designed to reduce short-term effects of air pollution on population health. However, whether air quality alert programme warnings, such as the AQHI, reduce morbidity is still questionable. Using a quasi-experimental design, we conducted the first evaluation of the AQHI in Hong Kong focusing on respiratory morbidity. Method Interrupted time series with Poisson segmented regression from 2010 to 2016 was used to detect any sudden or gradual changes in emergency respiratory hospital admissions, adjusted for air pollutants (NO2, SO2, PM10, O3), temperature and humidity, when the AQHI policy was implemented. The findings were validated using three false policy study periods and digestive diseases as a control. We also assessed effects on specific respiratory diseases (respiratory tract infections (RTI), asthma, chronic obstructive pulmonary disease and pneumonia) and by age. Results From January 1st, 2010 to December 31st, 2016, 10576.98 deseasonalized, age-standardized hospital admissions for respiratory disease occurred in Hong Kong. On implementation of the AQHI, RTI admissions immediately dropped by 14% (relative risk (RR) 0.86 95% confidence interval (CI) 0.76–0.98). In age specific analysis, immediate reductions in hospital admissions were only apparent in children for RTI (RR 0.84, 95% CI 0.74–0.96) and pneumonia (RR 0.88, 95% CI 0.60–0.96). Conclusion Hong Kong's AQHI helped reduced hospital admissions in children, particularly for RTI and pneumonia. To maximize health benefits of the policy, at risk groups need to be able to follow the behavioral changes recommended by the AQHI warnings. Graphical abstract

Development and application of an updated geospatial distribution model for gridding 2015 global mercury emissions Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): F. Steenhuisen, S.J. Wilson Abstract Mercury is a global pollutant that poses threats to ecosystems and to human health. Due to its global transport, mercury contamination is found in regions of the Earth that are remote from major emissions areas, including the Polar regions. Global anthropogenic emission inventories identify important sectors and industries responsible for emissions at a national level; however, to be useful for air transport modelling, more precise information on the locations of emission is required. This paper describes the methodology applied, and the results of work that was conducted to geospatially distribute anthropogenic mercury emissions as part of the global anthropogenic mercury emissions inventory for 2015 prepared by AMAP/UNEP for the Global Mercury Assessment (GMA) 2018 (UN Environment, 2019). This work includes the identification and use of emission point sources as well as distributing diffuse emissions for 21 emission (industry) sectors. The basic approach involves assigning emission estimates to geo-located point sources, using reported emissions information where available, and otherwise assigning a modelled emission to the point. Emissions which cannot be assigned to point sources are distributed using sector-specific proxies. Mercury speciation highly depends on industry processes and air pollution control technology. Different Hg speciation ratios are therefore applied per sector and country technology level. The resulting global emission datasets include total mercury (HgT), gaseous elemental mercury (Hg0), divalent mercury (Hg2+) and particulate mercury (HgP) in kg per year per raster cell (kg/a). The spatial resolution is flexible and the vertical resolution of the data is based on a set of predefined height classes. The resolution of the GMA2018 emission data is 0.25° × 0.25° with three (physical emission) height classes (0–50, 50–150 and > 150m). A comparison with the EMEP European mercury emission was made based on spatial correlation between the two datasets. Suggested improvements for future work include the further development of proxy data and the implementation of a more structured reporting of emissions by countries.

Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing–Tianjin–Hebei, China Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Xinpeng Wang, Wenbin Sun Abstract The deep learning model can simulate the complex nonlinear relationship between PM2.5 and aerosol optical depth (AOD), and has great application potentiality in PM2.5 inversion. However, the underestimation of high PM2.5 concentrations problem is still exist in heavily polluted Beijing–Tianjin–Hebei (JingJinJi) region due to AOD cannot adequately represent the correlation between high PM2.5 concentrations and independent variables and neglected the effects of missing AOD. Thus, the long- and short-term PM2.5 exposure risk estimate was reduced. This work introduces gaseous pollutant data (NO2, SO2, CO, and O3) related to primary emission and secondary transformation of pollutants as predictors into a deep neural network model to improve the underestimation of high PM2.5 concentrations based on AOD and meteorological factors. We predicted the PM2.5concentration in the missing AOD areas, generated a daily continuous PM2.5 spatial distribution, and reduced estimated bias due to AOD deficiency. Grid-based 10-fold cross-validation (CV) was used to test the model performance. Results show that daily PM2.5 concentration CV R2 is 0.87 and the root-mean-square prediction error (RMSE) is 27.11 μg/m3. The CV R2 and RMSE are higher by 0.12 and lower by 9.72 μg/m3 than the model without gaseous pollutants (GASS) as predictors. In including the missing AOD, the average concentration of PM2.5 CV R2 is 0.86 and the RMSE is 16.95 μg/m3 in heavy polluted winter; the CV R2 and RMSE are higher by 0.07 and lower by 3.95 μg/m3, respectively, than when the missing AOD was excluded. Prediction results of PM2.5 spatial distribution show that the model has high prediction accuracy and provides a complete and highly accurate spatiotemporal distribution characteristics for long- and short-term PM2.5 exposure studies, and reduces exposure misclassification of PM2.5 in heavily polluted areas. Graphical abstract

Characterization of trace aerosol compositions produced during the OH radical-initiated photooxidation of β-pinene Publication date: 15 August 2019 Source: Atmospheric Environment, Volume 211 Author(s): Peng Zhang, Jingyun Huang, Jinian Shu, Pengkun Ma, Bo Yang Abstract The detailed molecular composition of laboratory-generated secondary organic aerosols (SOA) during the high-NO photooxidation of β-pinene (βP) was investigated using a novel thermal desorption dichloromethane-assisted low-pressure photoionization mass spectrometry (TD-CH2Cl2-assisted LPPI-MS) technique. We found that protonated norpinone gave the highest signal intensity, which supported that assumption that this compound was the key oxidation species responsible for promoting βP-SOA formation. In addition to the previously identified monomers (i.e., 2,2-dimethylcyclobutyl-1,3-dicarboxaldehyde (m/z 140), pinene aldehyde (m/z150), myrtenol (m/z 152), (2,2-dimethyl-3-acetyl)-cyclobutylformate (m/z 156), pinene acid (m/z 166), and isomers of pinalic-3-acid (m/z 170)), two additional nitrogen-containing species (i.e., C10 hydroxy nitrate and C10carbonyl nitrate) were also observed at m/z values of 214 and 216 in the mass spectrum of βP-SOA. Furthermore, the losses of 18 (H2O), 46 (NO2), and 64 (H2O + NO2) were attributed to the typical fragmentation pathways of the two nitrogen-containing species. Overall, these results not only contribute to an improved understanding of βP-SOA formation, but they also indicate that TD-CH2Cl2-assisted LPPI-MS can be used as a novel detection means to study SOA formation.

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