Global warming both reduces global temperature variance and increases the frequency of extreme weather events. In response to these ambient perturbations, animals may be subject to trans- or intra-generational phenotype modifications that help to maintain homeostasis and fitness. Here, we show how temperature-associated transgenerational plasticity in tilapia affects metabolic trade-offs during developmental stages under a global warming scenario. Tropical tilapia reared at a stable temperature of 27 °C for a decade were divided into two temperature-experience groups for four generations of breeding. Each generation of one group was exposed to a single 15 °C cold-shock experience during its lifetime (cold-experienced CE group), and the other group was kept stably at 27 °C throughout their lifetimes (cold-naïve CN group). The offspring at early life stages from the CE and CN tilapia were then assessed by metabolomics-based profiling, and the results implied that parental cold-experience might affect energy provision during reproduction. Furthermore, at early life stages, progeny may be endowed with metabolic traits that help the animals cope with ambient temperature perturbations. This study also applied the feature rescaling and Uniform Manifold Approximation and Projection (UMAP) to visualize metabolic dynamics, and the result could effectively decompose the complex omic-based datasets to represent the energy trade-off variability. For example, the carbohydrate to free amino acid conversion and enhanced compensatory features appeared to be hypothermic-responsive traits. These multigenerational metabolic effects suggest that the tropical ectothermic tilapia may exhibit transgenerational phenotype plasticity, which could optimize energy allocation under ambient temperature challenges. Knowledge about such metabolism-related transgenerational plasticity effects in ectothermic aquatic species may allow us to better predict how adaptive mechanisms will affect fish populations in a climate with narrow temperature variation and frequent extreme weather events.

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.

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.

The association between temperature and mortality has been widely reported. However, it remains largely unclear whether inflammation-related diseases, caused by excessive or inappropriate inflammatory reaction, may be affected by ambient temperature, particularly in low-income areas.To explore the association between ambient temperature and clinical visits for inflammation-related diseases in rural villages in the Ningxia Hui Autonomous Region, China, during 2012─2015.Daily data on inflammation-related diseases and weather conditions were collected from 258 villages in Haiyuan (161 villages) and Yanchi (97 villages) counties during 2012─2015. A Quasi-Poisson regression with distributed lag non-linear model was used to examine the association between temperature and clinical visits for inflammation-related diseases. Stratified analyses were performed by types of diseases including arthritis, gastroenteritis, and gynecological inflammations.During the study period, there were 724,788 and 288,965 clinical visits for inflammation-related diseases in Haiyuan and Yanchi, respectively. Both exposure to low (RR: 2.045, 95% CI: 1.690, 2.474) and high temperatures (RR: 1.244, 95% CI: 1.107, 1.399) were associated with increased risk of total inflammation-related visits in Haiyuan county. Low temperatures were associated with increased risks of all types of inflammation-related diseases in Yanchi county (RR: 4.344, 95% CI: 2.887, 6.535), while high temperatures only affected gastroenteritis (RR: 1.274, 95% CI: 1.040, 1.561). Moderate temperatures explained approximately 26% and 33% of clinical visits due to inflammation-related diseases in Haiyuan and Yanchi, respectively, with the burden attributable to cold exposure higher than hot exposure. The reference temperature values ranged from 17 to 19 in Haiyuan, and 12 to 14 in Yanchi for all types of clinical visits.Our findings add additional evidence for the adverse effect of suboptimal ambient temperature and provide useful information for public health programs targeting people living in rural villages.

Traffic-generated ultrafine particles (UFPs) in the urban atmosphere have a high proportion of their composition comprised of semi-volatile compounds (SVOCs). The evaporation/condensation processes of these SVOCs can alter UFP number size distributions and play an important role in determining the fate of UFPs in urban areas. The neighbourhood-scale dispersion (over distances < 1 km) and evolution of traffic-generated UFPs for a real-world street network in central London was simulated by using the WRF-LES model (the large eddy simulation mode of the Weather Research and Forecasting modelling system) coupled with multicomponent microphysics. The neighbourhood scale dispersion of UFPs was significantly influenced by the spatial pattern of the real-world street emissions. Model output indicated the shrinkage of the peak diameter from the emitted profile to the downwind profile, due to an evaporation process during neighbourhood-scale dispersion. The dilution process and the aerosol microphysics interact with each other during the neighbourhood dispersion of UFPs, yielding model output that compares well with measurements made at a location downwind of an intense roadside source. The model captured the total SVOC concentrations well, with overestimations for gas concentrations and underestimations for particle concentrations, particularly of the lighter SVOCs. The contribution of the intense source, Marylebone Road (MR) in London, to concentrations at the downwind location (as estimated by a model scenario with emissions from MR only) is comparable with that of the rest of the street network (a scenario without emissions from MR), implying that both are important. An appreciable level of non-linearity is demonstrated for nucleation mode UFPs and medium range carbon SVOCs at the downwind receptor site.

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.

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM₂.₅ predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM₂.₅ relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM₂.₅, but few have been evaluated in terms of whether and to what extent they can help improve PM₂.₅ predictions. Therefore, an empirical and statistics-based scheme was developed for optimizing the estimation of the initial conditions (ICs) of aerosol in WRF-Chem (Weather Research and Forecasting/Chemistry) and for improving the PM₂.₅ predictions by integrating Himawari-8 data and ground observations. The proposed method was evaluated via two one-year experiments that were conducted in parallel over eastern China. The contribution of the satellite data to the model performance was evaluated via a 2-week control experiment. The results demonstrate that the proposed method improved the PM₂.₅ predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data.

This study examined the spatial and temporal variations of severe particulate pollution days (SPPDs) in China by using observed PM₂.₅ concentrations during April 2013 to February 2018 from the Ministry of Environmental Protection of China. SPPDs were defined as those with observed daily mean PM₂.₅ concentrations larger than 150 μg m⁻³. Observations showed that northern China had the highest number of SPPDs during the studied period. Since 2015, the number of SPPDs in northwestern China is comparable to or even higher than that observed in Beijing-Tianjin-Hebei (BTH). The highest numbers of SPPDs observed within BTH and the Yangtze River Delta (YRD) were 122 (33), 95 (17), 57 (15), 78 (18), and 31 (25) days in 2013, 2014, 2015, 2016, and 2017, respectively, indicating a general decreasing trend as a result of emission reduction measures. SPPDs occurred mainly from November to February in BTH and in December and January in the YRD. The major circulation patterns associated with large-scale SPPDs were analyzed by using principal component analysis. Five typical synoptic weather patterns were identified for BTH. The most dominant weather type (a cold high centered over the Xinjiang and Mongolian regions) for BTH was also responsible for most of the SPPDs in the YRD. These results have important implications for emission control strategies during SPPDs. Emission control measures can be applied once the dominant circulation patterns have been predicted.

PM₂.₅ pollution poses severe health risks to urban residents in low and middle-income countries. Existing studies have shown that the problem is affected by multiple socioeconomic factors. However, the relative contribution of these factors is not well understood, which sometimes leads to controversial controlling measures. In this study, we quantified the relative contribution of different socioeconomic factors, including the city size, industrial activities, and residents' activities, to PM₂.₅ pollution in urban China between 2014 and 2015 by using structural equation model (SEM). Our results showed that industrial activities contributed more to PM₂.₅ pollution than other factors. The city size and residents’ activities also had significant impacts on PM₂.₅ pollution. The combined influence of all socioeconomic factors could explain between 44% and 48% of variation in PM₂.₅ pollution, which indicated the existence of influences from other factors such as weather conditions and outside sources of pollutants. Findings from our study can contribute to a more comprehensive understanding of the socioeconomic causes of PM₂.₅ pollution.

The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables.We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.