A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM₂.₅ concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM₂.₅ from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM₂.₅ at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM₂.₅ for London in transport microenvironments were almost twice as high (corresponding to 13–20 μg/m³) as those for home and work environments (7–12 μg/m³). Analysis has shown that the PWM PM₂.₅ concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (−28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

Both air pollution and dyslipidemias contributed to large number of deaths and disability-adjusted life lost years. Long-term air pollution exposure was related to changed blood lipids and risk of dyslipidemias. This study was designed to evaluate relationships between air pollutants, blood lipids and prevalence of dyslipidemias in a Chinese rural population exposed to high-level air pollution based on baseline data of The Henan Rural Cohort study. An amount of 39,057 participants from rural areas in China were included. The 3-year average exposure of air pollutants (PM2.5, PM10, NO2) was estimated by a spatiotemporal model. Logistic and linear regression models were employed to explore relationships between air pollutants, blood lipids (TC, TG, HDL-C and LDL-C) and prevalence of dyslipidemias. The three-year concentration of PM2.5, PM10 and NO2 was 72.8 ± 2.3 μg/m3, 131.5 ± 5.7 μg/m3and 39.1 ± 3.1 μg/m3, respectively. Overall, increased air pollution exposure was related to increased TC and LDL-C, while decreased TG and HDL-C. Each 1-μg/m3 increment of PM2.5 was related to 0.10% (0.07%–0.19%) increase in TC, 0.63% (0.50%–0.77%) increase in LDL-C, 2.93% (2.70%–3.16%) decrease in TG, 0.49% (0.38%–0.60%) decrease in HDL-C; and 5.7% (95%CI: 3.7%–7.6%), 4.0% (95%CI: 2.1%–6.0%) and 3.8% (95%CI: 2.5%–5.1%) increase in odds for hypercholesterolemia, hyperbetalipoproteinemia and hypoalphalipoproteinemia, respectively. Stronger associations were found in male and older participants. Findings suggest that air pollutants were associated with changed blood lipid levels and higher risk of dyslipidemias among rural population. Male and elder people should pay more attention to personal safety protection.

Particulate air pollution is a continuing challenge in China, and its adverse effects on chronic obstructive pulmonary disease (COPD) have been widely reported. However, epidemiological evidence on the associations between size-fractionated particle number concentrations (PNCs) and COPD mortality is limited. In this study, we utilized a time-series approach to investigate the associations between PNCs of particles at 0.25–10 μm in diameter and COPD mortality in Shanghai, China. Quasi-Poisson regression generalized additive models were applied to evaluate these associations, with adjustment of time trend, day of week, holidays, temperature and relative humidity. Stratification analyses were performed by season and gender. There were a total of 3238 deaths due to COPD during the study period. We found that daily COPD deaths were significantly associated with PNCs of particles <0.5 μm, and the magnitude of associations increased with decreasing particle size. An interquartile range (IQR) increase in PNC₀.₂₅—₀.₂₈, PNC₀.₂₈—₀.₃, PNC₀.₃—₀.₃₅, PNC₀.₃₅—₀.₄, PNC₀.₄—₀.₄₅ and PNC₀.₄₅—₋₀.₅ was associated with increments of 7.51% (95%CI: 2.45%, 12.81%), 7.22% (95%CI: 2.16%, 12.53%), 6.95% (95%CI: 1.81%, 12.35%), 6.26% (95%CI: 1.25%, 11.52%), 5.24% (95%CI: 0.56%, 10.13%) and 4.15% (95%CI: 0.14%, 8.32%), respectively. The associations remained robustness after controlling for the mass concentrations of gaseous air pollutants. In stratification analyses, significant associations between PNCs and COPD mortality were observed in the cold seasons, and in males. Our results suggested that particles <0.5 μm in diameter might be most responsible for the adverse effects of particulate air pollution on COPD mortality, and COPD patients are more susceptible to PM air pollution in the cold seasons, especially for males.

The determination of the spatiotemporal patterns and driving factors of PM₂.₅ is of great interest to the atmospheric and climate science community, who aim to understand and better control the atmospheric linkage indicators. However, most previous studies have been conducted on pollution-sensitive cities, and there is a lack of large-scale and long-term systematic analyses. In this study, we investigated the spatiotemporal evolution of PM₂.₅ and its influencing factors by using an exploratory spatiotemporal data analysis (ESTDA) technique and spatial econometric model based on remote sensing imagery inversion data of the Yangtze River Economic Belt (YREB), China, between 2000 and 2016. The results showed that 1) the annual value of PM₂.₅ was in the range of 23.49–37.67 μg/m³ with an inverted U-shaped change trend, and the PM₂.₅ distribution presented distinct spatial heterogeneity; 2) there was a strong local spatial dependence and dynamic PM₂.₅ growth process, and the spatial agglomeration of PM₂.₅ exhibited higher path-dependence and spatial locking characteristics; and 3) the endogenous interaction effect of PM₂.₅ was significant, where each 1% increase in the neighbouring PM₂.₅ levels caused the local PM₂.₅ to increase by at least 0.4%. Natural and anthropogenic factors directly and indirectly influenced the PM₂.₅ levels. Our results provide spatial decision references for coordinated trans-regional air pollution governance as well as support for further studies which can inform sustainable development strategies in the YREB.

Exposure to air pollutants has been associated with respiratory and cardiovascular mortality, but the underlying molecular mechanisms remain inadequately understood. We aimed to examine molecular-level inflammatory and oxidative stress responses to personal air pollutant exposure. Fifty-three healthy adults aged 22–52 were measured three times for their blood inflammatory cytokines and urinary malondialdehyde (MDA, an oxidative stress biomarker) within 2 consecutive months. Pollutant concentrations monitored indoors and outdoors were combined with the time-activity data to calculate personal O₃, PM₂.₅, NO₂, and SO₂ exposures averaged over 12 h, 24 h, 1 week, and 2 weeks, respectively, prior to biospecimen collection. Inflammatory cytokines and MDA were associated with pollutant exposures using linear mixed-effects models controlling for various covariates. After adjusting for a co-pollutant, we found that concentrations of proinflammatory cytokines were significantly and negatively associated with 12-h O₃ exposures and significantly but positively associated with 2-week O₃ exposures. We also found significant and positive associations of proinflammatory cytokines with 12-h and 24-h NO₂ exposures, respectively. However, we did not find clear associations of PM₂.₅ and SO₂ exposure with proinflammatory cytokines and with MDA. The removal of an O₃-generating electrostatic precipitator in the mechanical ventilation systems of the offices and residences of the subjects was associated with significant decreases in IL-1β, IL-2, IL-6, IL-8, IL-17A, and TNF-α. These findings suggest that exposure to O₃ for different time durations may affect systemic inflammatory responses in different ways.

Ice and sediment cores, peat bogs and tree rings are useful proxy records for reconstructing historical air pollution events. However, these indirect measurements are subject to interferences caused by environmental perturbations including global climate change. Therefore, using multiple proxy records has advantages in constraining the analytical findings. In this study, we utilized the chronological record of atmospheric deposition preserved in vegetation succession ecosystems in the deglaciated region for reconstructing historical pollution events. The rate of Cd accumulation in the forest chronosequence zone was investigated in a deglaciated area of the Tibetan Plateau. The results obtained through this novel approach are consistent with the variations of Cd concentration recorded in tree-ring, showing a 4–7 times increase of atmospheric Cd deposition from the 1890s to the early 1970s followed by a decrease from the mid-1970s–2000s. The Cd pollution record indicates that elevated atmospheric Cd release occurred in regions of Southwest China and South Asia due to the rapid industrial development until 1970 followed by coordinated efforts in controlling air emissions after mid-1970s.

Carbon monoxide (CO) is an important gas that affects human health and causes air pollution. However, the estimates of CO emissions in China are still subject to large uncertainties. Based on the CO mass concentration and the coupled Weather Research and Forecast (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT), this study estimates the CO emissions over Zhengzhou, China. The results show that the mean CO mass concentration was 1.17 mg m⁻³ from November 2017 to February 2018, with a clear diurnal variation. There were two periods of rapidly increasing CO concentration in the diurnal variation, which are 06:00–09:00 and 16:00–20:00 local time. The footprint analysis shows that the observation site is highly influenced by local emissions. The most influential regions to the site observations are northeast and northwest Zhengzhou, which are associated with the geographical barrier of the Taihang Mountains in the north and narrow Fenwei Plain in the west. The inversion result shows that the actual emissions are lower than the inventory estimates. Using the optimal scaling factors, the WRF-STILT simulations of CO concentration agree closely with the CO measurements with the linear fitting regression equation y = 0.87x + 0.15. The slopes of the linear fitting regressions between the WRF-STILT-simulated CO concentrations determined using the optimal emissions and the observations range from 0.72 to 0.89 for four months, and all the fitting results passed the significance test (P < 0.001). These results indicate that the new optimal emissions derived with the scaling factors could better represent the real emission conditions than the a priori emissions if the WRF-STILT model is assumed to be reliable.

Food carts are common along streets in cities throughout the world. In North America, food cart vendors generally use propane, charcoal, or both propane and charcoal (P and C) for food preparation. Although cooking emissions are known to be a major source of indoor air pollution, there is limited knowledge on outdoor cooking’s impact on the ambient environment and, in particular, the relative contribution of the different cooking fuels. This field study investigated the air pollution the public is exposed to in the micro-environment around 19 food carts classified into 3 groups: propane, charcoal, and P and C carts. Concentrations near the food carts were measured using both real-time and filter-based methods. Mean real-time concentrations of PM₂.₅, BC₂.₅, and particle counts were highest near the charcoal food carts: 196 μg/m³, 5.49 μg/m³, and 69,000 particles/cm³, respectively, with peak exposures of 1520 μg/m³, 67.9 μg/m³, and 235,000 particles/cm³, respectively. In order of pollution emission impacts: charcoal > P and C > propane carts. Thus, significant differences in air pollution emissions occurred in the vicinity of mobile food carts, depending on the fuel used in food preparation. Local air pollution polices should consider these emission factors in regulating food cart vendor operations.

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.

Smoke from plastic waste incineration in an open air travels worldwide and is a major source of air pollution particulate matter (PM) that is very withstand to degradation and hazard to human health. Suspension of smoke aerosol components in water occurs during rains and fire extinguishing. Here, water-suspended plastic smoke aerosol (WPS) preparations suitable for biotesting were synthesized. It has been revealed using dynamic light scattering that WPS contained major nano-sized (∼30 nm) PM fraction, and this result was confirmed by electron microscopy. Optical absorption of WPS was in the UV region and an increase in λₑₓ led to a red-shift in fluorescence emission with a corresponding decrease in fluorescence intensity. WPS was analyzed in neurotoxicity studies in vitro using presynaptic rat cortex nerve terminals (synaptosomes). Generation of spontaneous reactive oxygen species (ROS) detected using fluorescent dye 2′,7-dichlorofluorescein in nerve terminals was decreased by WPS (10–50 μg/ml) in a dose-dependent manner. WPS also reduced the H₂O₂-evoked ROS production in synaptosomes, thereby influencing cellular oxidative processes and this effect was similar to that for carbon nanodots. WPS (0.1 mg/ml) decreased the synaptosomal membrane potential and synaptic vesicle acidification in fluorimetric experiments. WPS (1.0 mg/ml) attenuated the synaptosomal transporter-mediated uptake of excitatory and inhibitory neurotransmitters, L-[¹⁴C]glutamate and [³H]GABA, respectively. This can lead to an excessive increase in the glutamate concentration in the synaptic cleft and neurotoxicity via over activation of ionotropic glutamate receptors. Therefore, WPS was neurotoxic and provoked presynaptic malfunction through changes of oxidative activity, reduction of the membrane potential, synaptic vesicle acidification, and transporter-mediated uptake of excitatory and inhibitory neurotransmitters in nerve terminals. In summary, synthesis and emission to the environment of ultrafine PM occur during combustion of plastics, thereby polluting air and water resources, and possibly triggering development of neuropathologies.