Initiatives to displace petroleum and climate change mitigation have driven a recent increase in space heating with biomass combustion. However, there is ample evidence that biomass combustion emits significant quantities of health damaging pollutants. We investigated the near-source micro-environmental air quality impact of a biomass-fueled combined heat and power system equipped with an electrostatic precipitator (ESP) in Syracuse, NY. Two rooftop sampling stations with PM2.5 and CO2 analyzers were established in such that one could capture the plume while the other one served as the background for comparison depending on the wind direction. Four sonic anemometers were deployed around the stack to quantify spatially and temporally resolved local wind patterns. Fuel-based emission factors were derived based on near-source measurement. The Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model was then applied to simulate the spatial variations of primary PM2.5 without ESP. Our analysis shows that the absence of ESP could lead to an almost 7 times increase in near-source primary PM2.5 concentrations with a maximum concentration above 100 μg m−3 at the building rooftop. The above-ground “hotspots” would pose potential health risks to building occupants since particles could penetrate indoors via infiltration, natural ventilation, and fresh air intakes on the rooftop of multiple buildings. Our results demonstrated the importance of emission control for biomass combustion systems in urban area, and the need to take above-ground pollutant “hotspots” into account when permitting distributed generation. The effects of ambient wind speed and stack temperature, the suitability of airport meteorological data on micro-environmental air quality were explored, and the implications on mitigating near-source air pollution were discussed.

Volatile organic compounds (VOCs) from two sampling sites (HB and XB) in a power station centralized area, in Shuozhou city, China, were sampled by stainless steel canisters and measured by gas chromatography-mass selective detection/flame ionization detection (GC-MSD/FID) in the spring and autumn of 2014. The concentration of VOCs was higher in the autumn (HB, 96.87 μg/m3; XB, 58.94 μg/m3) than in the spring (HB, 41.49 μg/m3; XB, 43.46 μg/m3), as lower wind speed in the autumn could lead to pollutant accumulation, especially at HB, which is a new urban area surrounded by residential areas and a transportation hub. Alkanes were the dominant group at both HB and XB in both sampling periods, but the contribution of aromatic pollutants at HB in the autumn was much higher than that of the other alkanes (11.16–19.55%). Compared to other cities, BTEX pollution in Shuozhou was among the lowest levels in the world. Because of the high levels of aromatic pollutants, the ozone formation potential increased significantly at HB in the autumn. Using the ratio analyses to identify the age of the air masses and analyze the sources, the results showed that the atmospheric VOCs at XB were strongly influenced by the remote sources of coal combustion, while at HB in the spring and autumn were affected by the remote sources of coal combustion and local sources of vehicle emission, respectively. Source analysis conducted using the Positive Matrix Factorization (PMF) model at Shuozhou showed that coal combustion and vehicle emissions made the two largest contributions (29.98% and 21.25%, respectively) to atmospheric VOCs. With further economic restructuring, the influence of vehicle emissions on the air quality should become more significant, indicating that controlling vehicle emissions is key to reducing the air pollution.

There are limited ambient air measurements of extended (beyond EPA Priority 16) lists of polycyclic aromatic hydrocarbons (PAHs). We measured air concentrations of 45 PAHs using passive and active air sampling at 15 sites in a central urban community and one rural site for two years. Passive sampling was conducted with cylindrical XAD-based samplers deployed to capture spatial variability. High volume active samplers with quartz fiber filters for particles and XAD-4 absorbent for gases were deployed at two urban sites and the rural site to calibrate the passive measurements directly. Estimated passive sampling rates (PSRs) were evaluated as functions of meteorological data, seasons, locations, study year, and compared with other studies. Possible particle collection by the passive samplers was evaluated using a variety of particle measurements (TSP, PM10, PM2.5 and ultrafines <100 nm). Total PAHs were statistically associated with ultrafine particle concentrations and to a lesser extent PM2.5 and PM10, but not TSP. PSRs were more variable when PAH mass loadings were lower and near method detection limits; this occurred more often at the rural site. The PSRs were not statistically associated with meteorological conditions in this study, but wind speed had the highest potential to impact PSR results. The resulting passive PAH measurements are reported with respect to proximity to major roadways and other known air emissions types. PSRs were quantifiable for some PAHs that were found predominantly in the particulate phase in active sampling. This information, together with particle fraction calculations from active sampling, were used to estimate the particulate PAH capture of the passive sampler. Summed PAH (∑PAH) passive concentrations were measured within the range of 10–265 ng/m3, with the highest concentrations from naphthalene and the lowest detected concentrations from anthracene. These results indicated a stronger seasonal signal within 200 m of a major roadway.

Recent studies on regional haze pollution over China come up in general with strong variability of main causes of heavy polluted episodes, in linkage with local specificities, sources and pollution characteristics. This paper therefore aims at elucidating the main specific sources and formation mechanisms of observed strong haze pollution episodes over 1–15 November 2015 in Northeast region considered as one of biggest megacity clusters in China. The Northeast China mega-city cluster, including Heilong Jiang, Jilin and Liaoning provinces, is adjacent to Russia in the north, Mongolian at the west, North Korea at east, and representing key geographical location in the regional and transnational air pollution issues in China due to the presence of heavy industries and intense economic activities. The present study, based on air quality monitoring, remote sensing satellite data and sensitivity experiments carried on the Nested Air Quality Prediction Modeling System (NAQPMS), quantitatively assesses the impact of meteorological conditions and potential contributions from regional chemical transport, intensive energy combustion, illegal emission and biomass burning emissions to PM2.5 concentration variation. The results indicate strong inversion occurrence at lower atmosphere with weak near-surface wind speed and high relative humidity, leading to PM2.5 concentration increase of about 30–50%. Intensive energy combustion (plausibly for heating activities) and illegal emission also significantly enhance the overall PM2.5 accumulation by 100–200 μg m−3 (60–70% increase), against 75–100 μg m−3 from the biomass burning under the northeast-southwest transport pathway, corresponding to a contribution of 10–20% to PM2.5 concentration increase. Obviously, stagnant meteorological conditions, energy combustion, illegal emission and biomass burning are main drivers of strong haze formation and spatial distribution over Northeast China megacity cluster. In clear, much effort on emission abatement at both local and regional scales is still an urgent imperative to overcome current critical haze pollution.

Based on the 12-hour PM2.5 samples collected in an urban site of Beijing, sixteen PM2.5-bound Polycyclic Aromatic Hydrocarbons (PAHs) were measured to investigate the characteristics and potential source regions of particulate PAHs in Beijing. The study period included the summer period in July–August 2014, the APEC source control period during the Asia-Pacific Economic Cooperation (APEC) meeting in the first half of November 2014, and the heating period in the second half of November 2014. Compared to PM2.5, sum of 16 PM2.5-bound PAHs exhibited more significant seasonal variation with the winter concentration largely exceeding the summer concentration. Temperature appeared to be the most crucial meteorological factor during the summer and heating periods, while PM2.5-bound PAHs showed stronger correlation with relative humidity and wind speed during the APEC source control period. Residential heating significantly increased the concentrations of higher-ring-number (≥4) PAHs measured in PM2.5 fraction. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analysis as well as the (3 + 4) ring/(5 + 6) ring PAH ratio analysis revealed the seasonal difference in the potential source area of PM2.5-bound PAHs in Beijing. Southern Hebei was the most likely potential source area in the cold season. Using black carbon (BC) and carbon monoxide (CO) as the PAH tracers, regional residential, transportation and industry emissions all contributed to the PAH pollution in Beijing.

To investigate the spatial and temporal behaviors of particulate matter in Lanzhou, Jinchang and Jiayuguan during 2014, the hourly concentrations of PM2.5 and PM10 were collected from the Ministry of Environmental Protection (MEP) in this study. The analysis indicated that the mean annual PM10 (PM2.5) concentrations during 2014 were 115 ± 52 μg/m3 (57 ± 28 μg/m3), 104 ± 75 μg/m3 (38 ± 22 μg/m3) and 114 ± 72 μg/m3 (32 ± 17 μg/m3) in Lanzhou, Jinchang and Jiayuguan, respectively, all of which exceeded the Chinese national ambient air quality II standards for PM. Higher values for both PM fractions were generally observed in spring and winter, and lower concentrations were found in summer and autumn. Besides, the trend of seasonal variation of particulate matter (PM) in each city monitoring site is consistent with the average of the corresponding cities. Anthropogenic activities along with the boundary layer height and wind scale contributed to diurnal variations in PM that varied bimodally (Lanzhou and Jinchang) or unimodally (Jiayuguan). With the arrival of dust events, the PM10 concentrations changed dramatically, and the PM10 concentrations during dust storm events were, respectively, 19, 43 and 17 times higher than the levels before dust events in Lanzhou, Jinchang and Jiayuguan. The ratios (PM2.5/PM10) were lowest, while the correlations were highest, indicating that dust events contributed more coarse than fine particles, and the sources of PM are similar during dust storms. The relationships between local meteorological parameters and PM concentrations suggest a clear association between the highest PM concentrations, with T ≤ 7 °C, and strong winds (3–4 scale). However, the effect of relative humidity is complicated, with more PM10 and PM2.5 exceedances being registered with a relative humidity of less than 40% and 40–60% in Lanzhou, while higher exceedances in Jinchang appeared at a relative humidity of 80–100%.

China has frequently suffered regional-scale haze pollution in recent years. In this study, real-time observation data such as PM2.5, PM10, SO2, NO2, CO and O3 were used to analyze wintertime haze events at a rural site (Dongshan) in the Yangtze River Delta (YRD). 3-hour resolution organic carbon (OC) and elemental carbon (EC) were also measured to further investigate the sources of PM2.5. The hybrid receptor models were used to identify source regions of PM2.5. The results showed that both regional transport and local emissions significantly contribute to air pollution at Dongshan during haze periods. The source areas affecting high PM2.5 concentrations were mainly located in nearby urbanized provinces (i.e., Jiangsu, Anhui and Zhejiang) and industrial provinces (i.e., Shandong and Hebei) in eastern China. Furthermore, open biomass-burning emissions in south China (i.e., Jiangxi, Hunan, Guangdong and Fujian) decreased regional air quality, which was supported by MODIS fire spots and receptor models. During clean periods, air masses were originated from remote regions such as Mongolia and oceanic areas (i.e., the Yellow Sea and the East Sea). Enhanced secondary organic carbon (SOC) formation was found under long-range transport when OC aging was favorable. Contrarily, relatively low SOC formation was found when the site was dominated by local emissions. In addition to local emissions, high PM2.5 concentrations at Dongshan were apparently affected by either regional or long-range transport, which were characterized by relatively low and high wind speeds, respectively. It is necessary to implement the emission control strategies for the industrial and urbanized areas.

A model that describes the weathering of crude in an oil spill caused by interaction with the atmosphere and the ocean was developed. This model was adapted to the Colombian crudes Cusiana (°API43.2) and Vasconia (°API20.7). To calibrate the model, evaporation and emulsification experiments were carried out at conditions similar to those of an oil spill in the Colombian Caribbean Sea. The dependence of evaporation with wind velocity, not predicted by the state-of-the-art models, was captured by a correlation for the mass transfer coefficient calculated from the experimental data. Emulsification rate, maximum water content and required evaporation to form an emulsion were determined and their values explained considering the effect of wax precipitation for Cusiana crude oil. When compared to well-established weathering software, such as ADIOS, the proposed model predicts the weathering of Colombian oils in a way that better agrees with the experiments conducted in the laboratory.

This paper uses ARIMA models for daily temporal analysis of the effect of land surface coverage (LSC) on PM10 concentrations in a high-altitude megacity. Bogota, the capital of Colombia, is the urban center with the greatest population density and third-highest air pollution levels in Latin America. Six automatic monitoring stations were used; they were equipped with measurement instruments for PM10, temperature and solar radiation as well as wind speed and wind direction. The duration of the sampling period was 6 years. The hourly PM10 sampling system included continuous-monitoring equipment that used beta ray attenuation. We analyzed atmospheric stability and the spatial distribution of LSC (vegetated, non-vegetated, impervious and water bodies) before applying the iterative process of Box-Jenkins for ARIMA models. ARIMA analysis indicates greater persistence in PM10 pollution in the presence of increased vegetated LSC (trees and grasslands); persistence decreased in the presence of more impervious LSC (roofs, pavements and footpaths). PM10 persistence is found to be 2 days (48 h). The best distance to demonstrate these findings is between 50 and 100 m, with respect to the monitoring stations' physical location. Urban areas with a predominance of vegetated LSC register lower PM10 concentrations than urban areas with a predominance of impervious LSC (average daily difference = 42.7%). This study's findings serve as a reference point for the development of differentiated strategies for air pollution control in line with urban LSC.

A field survey to collect microplastics with sizes <5mm was conducted in the Southern Ocean in 2016. We performed five net-tows and collected 44 pieces of plastic. Total particle counts of the entire water column, which is free of vertical mixing, were computed using the surface concentration (particle count per unit seawater volume) of microplastics, wind speed, and significant wave height during the observation period. Total particle counts at two stations near Antarctica were estimated to be in the order of 100,000pieceskm−2.