Mitigation of ambient ozone (O₃) pollution is a great challenge because it depends heavily on the background O₃ which has been poorly evaluated in many regions, including in China. By establishing the relationship between O₃ and air temperature near the surface, the mean background O₃ mixing ratios in the clean and polluted seasons were determined to be 35–40 and 50–55 ppbv in China during 2013–2019, respectively. Simulations using the chemical transport model (i.e., the Weather Research and Forecasting coupled with Chemistry model, WRF/Chem) suggested that biogenic volatile organic compounds (VOC) emissions were the primary contributor to the increase in the background O₃ in the polluted season (BOP) compared to the background O₃ in the clean season (BOC), ranging from 8 ppbv to 16 ppbv. More importantly, the BOP continuously increased at a rate of 0.6–8.0 ppbv yr⁻¹ during 2013–2019, while the non-BOP stopped increasing after 2017. Consequently, an additional 2%–16% reduction in anthropogenic VOC emissions is required to reverse the current O₃ back to that measured in the period from 2013 to 2017. The results of this study emphasize the importance of the relative contribution of the background O₃ to the observed total O₃ concentration in the design of anthropogenic precursor emission control strategies for the attainment of O₃ standards.

Hybrid electric vehicle (HEV) technology is critical to reduce the impact of the internal combustion engines on air pollution and greenhouse gases. HEVs have an advantage in market penetration due to their lower cost and higher driving range compared to battery electric vehicles (BEVs). On the other hand, HEVs use an internal combustion engine and still emit air pollutants. It is hypothesized that HEV performance is impacted by the weather conditions as a result of many factors. It was beyond the scope of this work to systematically evaluate all factors so instead we measured emissions from two vehicles driving city and highway routes in Minneapolis, Minnesota in the winter (−5 °C) and looked for major differences in emissions relative to each vehicle and relative to results that would be obtained from a chassis dynamometer in a controlled laboratory setting at a higher temperature approximately 20 °C). The study then looked to associate differences in emissions with the prevailing conditions to gain new insights. Emissions of interest included the total particle number (TPN), solid particle number (SPN), particulate matter mass (PM), and NOx. One key difference in vehicle engine technology was PFI (port fuel injection) versus GDI (gasoline direct injection). We found the frequency at which the Prius hybrid engine reignited was much higher than the Sonata for city and highway driving, although for both vehicles the catalyst temperature remained high and appeared to be unaffected by the reignitions, despite the cold weather. For most conditions, the Prius emitted more NOₓ but fewer particles than the Sonata. In some cases, NOₓ and particle emissions exceeded the most comparable laboratory-based emissions standards.

Atmospheric aerosols play a crucial role in climate change, especially in the Himalayas and Tibetan Plateau. Here, we present the seasonal and diurnal characteristics of aerosol vertical profiles measured using a Mie lidar, along with surface black carbon (BC) measurements, at Mt. Qomolangma (QOMS), in the central Himalayas, in 2018–2019. Lidar-retrieved profiles of aerosols showed a distinct seasonal pattern of aerosol loading (aerosol extinction coefficient, AEC), with a maximum in the pre-monsoon (19.8 ± 22.7 Mm⁻¹ of AEC) and minimum in the summer monsoon (7.0 ± 11.2 Mm⁻¹ of AEC) seasons. The diurnal variation characteristics of AEC and BC were quite different in the non-monsoon seasons with enriched aerosols being maintained from 00:00 to 10:00 in the pre-monsoon season. The major aerosol types at QOMS were identified as background, pollution, and dust aerosols, especially during the pre-monsoon season. The occurrence of pollution events influenced the vertical distribution, seasonal/diurnal patterns, and types of aerosols. Source contribution of BC based on the weather research and forecasting chemical model showed that approximately 64.2% ± 17.0% of BC at the QOMS originated from India and Nepal in South Asia during the non-monsoon seasons, whereas approximately 47.7% was from local emission sources in monsoon season. In particular, the high abundance of BC at the QOMS in the pre-monsoon season was attributed to biomass burning, whereas anthropogenic emissions were the likely sources during the other seasons. The maximum aerosol concentration appeared in the near-surface layer (approximately 4.3 km ASL), and high concentrations of transported aerosols were mainly found at 4.98, 4.58, 4.74, and 4.88 km ASL in the pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively. The investigation of the vertical profiles of aerosols at the QOMS can help verify the representation of aerosols in the air quality model and satellite products and regulate the anthropogenic disturbance over the Tibetan Plateau.

Light absorbing carbonaceous aerosols (LACA) consisting of black carbon (BC) and brown carbon (BrC) have received considerable attention because of their climate and health implications, but their sources, characteristics and fates remain unclear in Southeast Asia (SEA). In this study, we investigated spatio-temporal characteristics of LACA, their radiative properties and potential sources in Singapore under different weather conditions. Hourly BC concentrations, measured from May 2017 to March 2018, ranged from 0.31 μg/m³ to 14.37 μg/m³ with the mean value being 2.44 ± 1.51 μg/m³. High mass concentrations of BC were observed during the south-west monsoon (SWM, 2.60 ± 1.56 μg/m³) while relatively low mass concentrations were recorded during the north-east monsoon (NEM, 1.68 ± 0.96 μg/m³). There was a shift in the Absorption Ångström exponent (AAE) from 1.1 to 1.4 when the origin of LACA changed from fossil fuel (FF) to biomass burning (BB) combustion. This shift is attributed to the presence of secondary BrC in LACA, derived from transboundary BB emissions during the SWM. Lower AAE values were observed when local traffic emissions were dominant during the NEM. This explanation is supported by measurements of water-soluble organic carbon (WSOC) in LACA and the corresponding AAE values determined at 365 nm using a UV–vis spectrophotometer. The AAE values, indicative of the presence of brown carbon (BrC), showed that photochemically aged LACA contribute to an enhancement in the light absorption of aerosols. In addition, spatio-temporal characteristics of BC in the intra-urban environment of Singapore were investigated across diverse outdoor and indoor microenvironments. High variability of BC was evident across these microenvironments. Several air pollution hotspots with elevated BC concentrations were identified. Overall, the results stress a need to control anthropogenic emissions of BC and BrC in order to mitigate near-term climate change impacts and provide health benefits.

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.

At the Pearl River Estuary of southern China, mercury and its environmental problems have long been a great concern. This study investigated the distribution and speciation of mercury compounds that are significantly influenced by the increasing content of humic acid (HA, a model natural organic matter) in this region. The inorganic mercury and methyl mercury, being adsorbed and converted at different HA levels, were studied in sediments and surface water at both mariculture and their reference sites. In mariculture sediments with higher HA content (up to 4.5%), more mercury were adsorbed at different compound levels, promoting the methylation and accumulation of mercury (P < 0.05) at the sediment-water interface. Seasonal shift in environmental temperature might control the HA content, subsequently favouring mercury methylation (maximum 1.75 ± 0.08 mg L−1 d−1) under warm weather conditions. In reference sites received less HA wastes, lower adsorption capacity and methylation rate were observed for mercury in sediments and surface water. Our work points to the significant roles of HA on mercury distribution and speciation both spatially and seasonally, thus addressing the impacts of mariculture activities on estuary eco-system.

Emissions from the transportation sector are rapidly changing worldwide; however, the interplay of such emission changes in the face of climate change are not as well understood. This two-part study examines the impact of projected emissions from the U.S. transportation sector (Part I) on ambient air quality in the face of climate change (Part II). In Part I of this study, we describe the methodology and results of a novel Technology Driver Model (see graphical abstract) that includes 1) transportation emission projections (including on-road vehicles, non-road engines, aircraft, rail, and ship) derived from a dynamic technology model that accounts for various technology and policy options under an IPCC emission scenario, and 2) the configuration/evaluation of a dynamically downscaled Weather Research and Forecasting/Community Multiscale Air Quality modeling system.By 2046–2050, the annual domain-average transportation emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH3), and sulfur dioxide (SO2) are projected to decrease over the continental U.S. The decreases in gaseous emissions are mainly due to reduced emissions from on-road vehicles and non-road engines, which exhibit spatial and seasonal variations across the U.S. Although particulate matter (PM) emissions widely decrease, some areas in the U.S. experience relatively large increases due to increases in ship emissions. The on-road vehicle emissions dominate the emission changes for CO, NOx, VOC, and NH3, while emissions from both the on-road and non-road modes have strong contributions to PM and SO2 emission changes. The evaluation of the baseline 2005 WRF simulation indicates that annual biases are close to or within the acceptable criteria for meteorological performance in the literature, and there is an overall good agreement in the 2005 CMAQ simulations of chemical variables against both surface and satellite observations.

A Single Particle Aerosol Mass Spectrometer (SPAMS) was deployed in the urban area of Chongqing to characterize the particles present during a severe particulate pollution event that occurred in winter 2014–2015. The measurements were made at a time when residents engaged in traditional outdoor meat smoking activities to preserve meat before the Chinese Spring Festival. The measurement period was predominantly characterized by stagnant weather conditions, highly elevated levels of PM2.5, and low visibility. Eleven major single particle types were identified, with over 92.5% of the particles attributed to biomass burning emissions. Most of the particle types showed appreciable signs of aging in the stagnant air conditions. To simulate the meat smoking activities, a series of controlled smoldering experiments was conducted using freshly cut pine and cypress branches, both with and without wood logs. SPAMS data obtained from these experiments revealed a number of biomass burning particle types, including an elemental and organic carbon (ECOC) type that proved to be the most suitable marker for meat smoking activities. The traditional activity of making preserved meat in southwestern China is shown here to be a major source of particulate pollution. Improved measures to reduce emissions from the smoking of meat should be introduced to improve air quality in regions where smoking meat activity prevails.

Comprehensive measurements were conducted at the summit of Mount (Mt.) Huang, a rural site located in eastern China during the summer of 2011. They observed that ozone showed pronounced diurnal variations with high concentrations at night and low values during daytime. The Weather Research and Forecasting with Chemistry (WRF-Chem) model was applied to simulate the ozone concentrations at Mt. Huang in June 2011. With processes analysis and online ozone tagging method we coupled into the model system, the causes of this diurnal pattern and the contributions from different source regions were investigated. Our results showed that boundary layer diurnal cycle played an important role in driving the ozone diurnal variation. Further analysis showed that the negative contribution of vertical mixing was significant, resulting in the ozone decrease during the daytime. In contrast, ozone increased at night owing to the significant positive contribution of advection. This shifting of major factor between vertical mixing and advection formed this diurnal variation. Ozone source apportionment results indicated that approximately half was provided by inflow effect of ozone from outside the model domain (O3-INFLOW) and the other half was formed by ozone precursors (O3-PBL) emitted in eastern, central, and southern China. In the O3-PBL, 3.0% of the ozone was from Mt. Huang reflecting the small local contribution (O3-LOC) and the non-local contributions (O3-NLOC) accounted for 41.6%, in which ozone from the southerly regions contributed significantly, for example, 9.9% of the ozone originating from Jiangxi, representing the highest geographical contributor. Because the origin and variation of O3-NLOC was highly related to the diurnal movements in boundary layer, the similar diurnal patterns between O3-NLOC and total ozone both indicated the direct influence of O3-NLOC and the importance of boundary layer diurnal variations in the formation of such distinct diurnal ozone variations at Mt. Huang.

The effects of heatwaves on morbidity in developing and tropical countries have not been well explored. The purpose of this study was to examine the relationship between heatwaves and hospitalization and the potential influence of socio-economic factors on this relationship in Vietnam.Generalized Linear Models (GLM) with Poisson family and Distributed Lag Models (DLM) were applied to evaluate the effect of heatwaves for each province (province-level effect). A random-effects meta-analysis was applied to calculate the pooled estimates (country-level effects) for ‘all causes’, infectious, cardiovascular, and respiratory admissions queried by lag days, regions, sex, and ages. We used random-effects meta-regression to explore the potential influence of socio-economic factors on the relationship between heatwaves and hospitalization.The size of province-level effects varied across provinces. The pooled estimates show that heatwaves were significantly associated with a 2.5% (95%CI: 0.8–4.3) and 3.8% (95%CI, 1.5–6.2) increase in all causes and infectious admissions at lag 0. Cardiovascular and respiratory admissions (0.8%, 95%CI: −1.6–3.3; 2.2%, 95%CI: −0.7–5.2) were not significantly increased after a heatwave event. The risk of hospitalization due to heatwaves was higher in the North than in the South for all causes (5.4%, 95%CI: −0.1–11.5 versus 1.3%, 95%CI: 0.1–2.6), infectious (11.2%, 95%CI: 3.1–19.9 versus 3.2%, 95%CI: 0.7–5.7), cardiovascular (7.5%, 95%CI: 1.1–14.4 versus −1.2%, 95%CI: −2.6–2.3), and respiratory diseases (2.7%, 95%CI: −5.4–11.5 versus 2.1%, 95%CI: −0.8–1.2). A non-significant influence of socio-economic factors on the relationship between heatwave and hospitalization was observed.This study provides important evidence and suggests implications for the projected impacts of climate change related extreme weather. Climate change adaptation programs of the health sector should be developed to protect residents from the effects of extreme weather events such as heatwaves in Vietnam.