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The Pearl River Delta (PRD) region is highly vulnerable to tropical cyclone (TC)-caused coastal hazards due to its long and meandering shoreline and well-developed economy. With global warming expected to continue or worsen in the rest of the twenty-first century, this study examines the TC impact on the PRD coastal regions by reproducing three intense landfalling TCs, namely Vicente (2012), Hato (2017), Mangkhut (2018), using a sophisticated air-wave-ocean coupled model of high spatial resolution (1-km atmosphere and 500-m wave and ocean). The simulations are conducted using present-day reanalysis data and the same TCs occurring in a pseudo-global warming scenario projected for the 2090s. Results indicate that the coupled model accurately reproduces the air-wave-ocean status during the TC episodes. The 2090s thermodynamic status effectively increases the intensity of intense TCs, leading to more severe coastal hazards including gale, rainstorm, and storm surges and waves. On average, the maximum surface wind speed within 50–200 km to the right of the TC center can increase by 4.3 m/s (+22%). The 99th and the 99.9th percentile of accumulated rainfall will increase from 405 to 475 mm (+17.3%), and from 619 to 735 mm (+18.6%), respectively. The maximum significant wave height at the ocean is lifted by an average of 57 cm (+13.8%), and the coastline typically faces a 40–80 cm increase. The maximum storm surges are lifted by 30–80 cm over the open sea but aggravate much higher along the coastline, especially for narrowing estuaries. For Typhoon Vicente (2012), there is more than a 200 cm wave height increase observed both at open sea and along the coastline. In the 2090s context, a combination of mean sea level rise, storm surge, and wave height can reach more than 300 cm increase in total water level at certain hot-spot coastlines, without considering the superposition of spring tides.

Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin-Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post-processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post-processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Initial success has been achieved in Hong Kong in controlling primary air pollutants, but ambient ozone levels kept increasing during the past three decades. Volatile organic compounds (VOCs) are important for mitigating ozone pollution as its major precursors. This study analyzed VOC characteristics of roadside, suburban, and rural sites in Hong Kong to investigate their compositions, concentrations, and source contributions. Here we show that the TVOC concentrations were 23.05 ± 13.24, 12.68 ± 15.36, and 5.16 ± 5.48 ppbv for roadside, suburban, and rural sites between May 2015 to June 2019, respectively. By using Positive Matrix Factorization (PMF) model, six sources were identified at the roadside site over five years: Liquefied petroleum gas (LPG) usage (33–46%), gasoline evaporation (8–31%), aged air mass (11–28%), gasoline exhaust (5–16%), diesel exhaust (2–16%) and fuel filling (7–9%). Similarly, six sources were distinguished at the suburban site, including LPG usage (30–33%), solvent usage (20–26%), diesel exhaust (14–26%), gasoline evaporation (8–16%), aged air mass (4–11%), and biogenic emissions (2–5%). At the rural site, four sources were identified, including aged air mass (33–51%), solvent usage (25–30%), vehicular emissions (11–28%), and biogenic emissions (6–12%). The analysis further revealed that fuel filling and LPG usage were the primary contributors to OFP and OH reactivity at the roadside site, while solvent usage and biogenic emissions accounted for almost half of OFP and OH reactivity at the suburban and rural sites, respectively. These findings highlight the importance of identifying and characterizing VOC sources at different sites to help policymakers develop targeted measures for pollution mitigation in specific areas.

Precise and timely rainfall nowcasting plays a critical role in ensuring public safety amid disasters triggered by heavy precipitation. While deep-learning models have exhibited superior performance over traditional nowcasting methods in recent years, their efficacy is still hampered by limited forecasting skill, insufficient training data, and escalating blurriness in forecasts. To address these challenges, we present the Synthetic-data Task-segmented Generative Model (STGM), an innovative physical-dynamic-driven heavy rainfall nowcasting model. The STGM encompasses three key components: the Long Video Generation (LVG) model generating synthetic radar data from observed radar images and data provided by the Weather Research and Forecasting (WRF) model, MaskPredNet predicting the spatial coverage of various rainfall intensities, and SPADE determining rainfall intensity based on the coverage provided by MaskPredNet. The STGM has demonstrated promising skill for precipitation forecasts for up to six hours, and significantly reduce the blurriness of predicted images, thus showcasing advances in rainfall nowcasting. © 2023 The Authors.

Previous studies on convective precipitation forecasting in South China have focused on the effects of multiscale dynamics and microphysics parameterizations. However, limited investigation has been conducted on how uncertainty in aerosol data might cause errors in quantitative precipitation forecast for South China's coastal convection. In this case study, we evaluated the impact of aerosol uncertainties on South China's severe coastal convection using convection-permitting simulations. We estimated the variability range of aerosol concentrations with observations for the pre-summer months. The simulation results suggest that the rainfall pattern and intensity change notably when aerosol concentrations are varied. Decreasing the concentration of water-friendly (WF) aerosols intensifies precipitation through reduced cloud water number concentration and increased droplet size. Increasing the concentration of ice-friendly (IF) aerosols results in up to 40% increase in vertical velocity and latent heat compared to minimal IF aerosol condition, by enhancing the heterogeneous process and dynamically intensifying convection. Consequently, the simulation with minimal WF and maximal IF aerosol concentrations shows prolonged intense precipitation over the entire life cycle of convection. However, when both WF and IF aerosols are set to minimal concentrations, the simulation produces the maximum peak rainfall rate, which is about 50% stronger than the simulation with the climatological mean concentration, due to an enhanced homogeneous process that results in a higher ice concentration and more efficient ice-phase precipitation growth. Meanwhile, variation in aerosol concentration affects convection initiation (CI), with a lower concentration of WF aerosol inducing earlier CI onset. Decreasing hygroscopicity leads to higher precipitation.

Trace metals in fine particulate matter (PM2.5) are of significant concern in environmental chemistry due to their toxicity and catalytic capability. An observation-constrained hybrid model is developed to resolve regional source contributions to trace metals and other primary species in PM2.5. In this method, source contributions to primary PM2.5 (PPM2.5) from the Community Multiscale Air Quality (CMAQ) Model at each monitoring location are improved to align better with the observation data by applying source-specific scaling factors estimated from a unique regression model. The adjusted PPM2.5 predictions and chemical speciation data are then used to generate observation-constrained source profiles of primary species. Finally, spatial distributions of their source contributions are produced by multiplying the improved CMAQ PPM2.5 contributions with the deduced source profiles. The model is applied to the Pearl River Delta (PRD) region, China using daily observation data collected at multiple stations in 2015 to resolve source contributions to 8 trace metals, elemental carbon, primary organic carbon, and 10 other primary species. Compared to three previous methods, the new model predicts 13 species with smaller model errors and 16 species with less model biases in 10-fold cross validation analysis. The source profiles determined in this study also show good agreement with those collected from the literature. The new model shows that during 2015 in the PRD region, Cu is mainly from the area sources (31%), industry sector (27%), and power generation (20%), with an annual average concentration as high as 50 ng m−3 in some districts. Meanwhile, major contributors to Mn are area sources (40%), emission from outside PRD (23%) and power generation (17%), leading to a mean level of around 10 ng m−3. Such information is essential in assessing the epidemiological impacts of trace metals as well as formulating effective control measures to protect public health.

Precise quantification of precipitation is crucial for effective planning and minimizing property damage or loss of human life caused by extreme weather events, especially under the rapidly changing climate. Currently, quantitative precipitation forecasting (QPF) in numerical weather prediction (NWP) models rely heavily on parameterization schemes for microphysics, boundary layers, cumulus, etc., rather than directly solving physical-based governing equation sets to predict fundamental variables such as temperature, wind speed, and humidity. These parameterization schemes introduce significant uncertainties in precipitation forecasting due to the limited knowledge of precipitation processes, which bottlenecks the performance of precipitation forecasting in NWP models. To overcome this challenge, we propose a deep learning model based on Vision-Transformer that directly ingests fundamental meteorological variables solved by NWP models as predictors and maps them quantitatively to the precipitation map from a satellite-merged precipitation product. In this study, we conducted Weather Research and Forecasting (WRF) model simulations at 27km grid resolution for five years from 2017 to 2021 over China and the southeast region of Asia, and we used simulation results for the wettest season from June to September in 2017-2019 as training data, while validating and testing the model performance on data from 2020 and 2021. The deep learning model aims to circumvent uncertainties in physical parameterization schemes, which are due to the incomplete understanding of physical processes, and directly reproduce the high-resolution satellite rainfall observation product, the Climate Prediction Center morphing method (CMORPH) data. Our evaluation results on the test dataset show that the deep learning model effectively extracts features from meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy ra

Nitrogen oxides (NOx, mainly comprising NO and NO2) is the essential precursor of secondary air pollutants, such as ozone and particulate nitrate. To better understand NOx emission levels and acquire reasonable simulation results for further analysis, a reasonable emission inventory is needed. In this study, a new method, combining the three-dimensional chemical transport model simulation, surface NO2 observations, the three-dimensional variational assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error and normalized mean bias decreased by approximately 40% and 60% in the NO2 simulation using posterior NOx emissions. Compared with the emissions for 2015, the NOx emission generally reduced by an average of 5% in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24% and 19%, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. It can provide policymakers and academic researchers with the latest emission information for better emission control and air pollution research.

Black carbon (BC) and brown carbon (BrC) have been considered light-absorbing components of particulate matter and affect weather and climate. Biomass burning (BB) emission from Southeast Asia (SEA) is a key source of BC and BrC on the planet. In this study, the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) two-way coupled model was used with the Global Fire Emissions Database Version 4, to investigate the direct radiative effect (DRE) of BC and BrC in March 2015 over SEA. The Rapid Radiative Transfer Model for the Global Circulation Model was employed in the WRF-CAMQ to calculate the aerosol optical properties in 14 shortwave spectral bands. Parameterization of the light absorption property of BrC described by Saleh et al. (2014) is coded and embedded into the WRF-CMAQ. The light absorption property of BrC is determined by the BB BC to organic carbon ratio in each grid and each time step, which is more in line with the smog chamber experiments compared to the originally fixed coefficient in the model. Experiments with and without BC/BrC DRE were conducted. Preliminary results show that the monthly mean DRE from BB BC can reach 18.3 W/m2 in the Indochina region and 3.0 W/m2 in southern China, decreasing the surface temperature by up to 0.2 and 0.1 °C, respectively. The monthly DRE from BB BrC can reach 1.3 W/m2 in the Indochina region but only around 0.1 W/m2 in southern China. Meanwhile, the maximum instant DRE of BrC can reach 10.0 W/m2, which is expected to exert a local synoptic scale influence.

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM2.5 and 1040 pseudo networks for NO2. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO2 less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits

People now spend between 80% and 90% of their lifetimes in indoor locations such as offices and residential buildings. These extended hours indoors have substantial health impacts, making it vital that people have adequate indoor air quality (IAQ). The management of IAQ presents two issues: (1) rapidly predicting and controlling IAQ using current intelligent ventilation systems is difficult, and (2) simultaneously controlling both gas pollutants (of which CO2 is a representative example) and particulate matter concentrations (for example, PM2.5) while achieving optimal outcomes is challenging. Therefore, this study aims to develop a fast and accurate optimization algorithm to simultaneously predict and control indoor CO2 and PM2.5 concentrations. To this end, a back-propagation neural network (BPNN) combined with an adaptive multi-objective particle swarm optimizer (AMOPSO) algorithm based on computational fluid dynamics (CFD) is proposed. The CFD model first creates a database of indoor CO2 and PM2.5 concentrations. Then, based on the CFD database, the BPNN model is used to predict indoor air pollutant concentrations. If the predicted concentrations do not meet predetermined limits, the AMOPSO algorithm is initialized to optimize the concentrations of the indoor air pollutants. In test examples, the proposed optimization algorithm reduces CO2 concentrations by up to 30.5%, while also reducing PM2.5 concentrations by as much as 77.1%.

Ambient air pollution is a major environmental health risk in many cities, leading to millions of premature deaths per year globally. The regulation and management of urban air pollution is currently based on two pillars. On the one hand, a limited number of fixed site monitoring stations provides highly accurate measurement data but cannot resolve the high spatial pollution heterogeneity encountered in urban areas. On the other hand, computer models provide high-resolution concentration maps, but are bound by static emission inventories and lower time resolutions. The rise and nature of citizen-based applications, which are aimed at empowering individuals to make informed choices for their health, pose expanded requirements on air quality monitoring and modelling not covered by the previous paradigm. For example, applications such as finding one’s least polluted way through a city require real-time data of roadside pollution at a high spatial resolution accessible to the general public. Towards this goal, we firstly • assessed the ability of Hong Kong’s current air quality network, made up of fixed site monitors, to represent the population-based health effects of air pollution. Combining high-resolution air quality model, spatial population distribution and health risk factors, we propose a novel population-health based network representation index. We found that the current monitoring network reflects health risks well for particulates but is less able to represent risks for NO2 and O3. Subsequently, we performed two studies aimed at the integration of new data sources: • Based on these findings, we tested whether the identified shortcomings of the current network can be improved by the addition of smart sensors. These sensors can be characterized by a low price and simple installation but also suffer from measurement errors. We conducted a model-based study, in which up to 400 pseudo smart sensors were perturbated with the aim of simulating

Both greenhouse gas (GHG) emissions and urban expansion are regarded as important contributors to accelerating warming trends. Southeastern China, where Pearl River Delta (PRD) and Yangtze River Delta (YRD) are nestled, is a representative region with mounting concerns related to anthropogenic warming and the rapid pace of urbanization. Since these places have already experienced thermal discomfort at dangerous levels, it is not farfetched to infer that further warming will increase the heat stress close to the lethal level of human adaptability. However, most climate projections are yet to adequately consider future urban growth and their synergistic effects under global warming, leading to a potential underestimation of the risk of heat-related extremes. This study adopts the latest version of the Regional Climate Model (RegCM) for dynamical downscaling to better resolve geographically diverse climate features and then address the aforementioned limitations. Its performance has been improved by optimizing the configurations over target domains and incorporating a new urban module. First, the double-nested modeling system is developed focusing on the PRD and YRD regions, and its capability to capture region-specific climate characteristics is intensively evaluated in order to assess the effects of convection-permitting and non-hydrostatic dynamical core. Then, the land surface parameterization in RegCM is improved by incorporating a building energy model to prognostically account for the interior building temperatures, thereby enhancing the estimation of released anthropogenic heat fluxes into the urban canopy. Finally, the multiple global climate projections forced by both medium as well as high levels emission scenarios (i.e., RCP4.5 and RCP8.5) are dynamically downscaled using RegCM double-nested system with the improved urban parameterization by prescribing future urban expansion generated from different Shared Socio-Economic Pathways (i.e., SSP2 and SSP5). The

Maritime shipping is under increasing pressure to alleviate its environmental impact. To this end, life-cycle footprint accounting provides a foundation for taking targeted measures in the green transition of shipping. This study used the process-based hybrid life-cycle inventory (LCI) modeling approach to estimate the “cradle-to-propeller” footprint of ships, including primary energy consumption, carbon dioxide emissions, and sulfur dioxide emissions. We used the input–output LCI model to calculate the embodied energy and emissions associated with the material and fuel use of ship manufacturing. We used a bottom-up emission model and global marine traffic data to estimate the operational footprint of different types of ships. Based on 382 cargo ships (including bulk carriers, container ships, and general cargo ships) constructed in mainland China between 2011 and 2015, we estimated that the embodied footprint accounted for <10% of the cradle-to-propeller footprint under the pre-2019 policy scenario. In terms of life-cycle energy intensity (MJ/nm/1000 deadweight tonnage [DWT]), the large bulk carrier (>100,000 DWT) establishes the lowest value (46), followed by the small (0–100,000 DWT) bulk carrier (96), the large container ship (133), the small container ship (196), and the small general cargo (238). The bulk carrier was identified as the most energy efficient among the three ship types, and ships with larger capacities (i.e., DWT) had higher energy efficiencies than ships with lower capacities. Our study provides a comprehensive understanding of the life-cycle footprints of cargo ships, thus enabling better evidence-based policymaking to transition the global marine-shipping industry to a future of greener energy.

Deep-learning frameworks can effectively forecast the air pollution data for individual stations by decoding time-series data. However, most of the existing time-series-based deep-learning models use offline spatial interpolation strategies and thus cannot reliably project the station-based forecast to the spatial region of interest. In this study, the station-based long short-term memory (LSTM) technique was extended for spatial air quality forecasting by combining a novel deep-learning layer termed the broadcasting layer, which incorporates a learnable weight decay parameter designed for point-to-area extension. Unlike most existing deep-learning-based methods that isolate the interpolation from the model training process, the proposed end-to-end LSTM-broadcasting framework can consider the temporal characteristics of the time series and spatial relationships among different stations. To validate the proposed deep-learning framework, PM2.5 and O3 forecasts for the next 48 h were obtained using 3D chemical transport model simulation results and ground observation data as the inputs. The root mean square error associated with the proposed framework was 40 % and 20 % lower than those of the Weather Research Forecast–Community Multiscale Air Quality model and an offline combination of the deep-learning and spatial interpolation methods, respectively. The novel LSTM-broadcasting framework can be extended for air pollution forecasting in other regions of interest.

Poor indoor air quality contributes to poor respiratory health at an individual and public health level, with subsequent economic effects. These problems could be tackled by improving ventilation. While computational fluid dynamics simulations can model air flow in a room and identify ventilation issues, calculations are computationally expensive, and changing parameters can yield little to no improvement or even worsening of ventilation. A required iterative process is less than ideal for static environments and too expensive for widespread application. To approach this problem, the aim of this project was to generate sufficiently rapid simulation predictions that could incorporate changes inside the rooms environment, an approach which requires a focus on velocity patterns. Since speeding up conventional methods is found to be inadequate, artificial intelligence was utilized as an enabling technology. A two dimensional parameterized interface was chosen to speed up dataset generation and simplify data processing. Out-of-sample fluid flows could be predicted with an average coefficient of multiple correlation of 0.5. The out-of-sample mean average percentage error strongly indicated a lack of features for machine learning, with the flow components in the Y direction being just below 5%, while that of the X components were approximately 57%. Overall, this approach emphasized improvements in calculation time over accuracy. The average calculation time reduced from approximately 40 hours for conventional calculations, to around 4 seconds using a trained network. Future investigations will optimize the methodology to improve both accuracy and computation time further.

An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, a nonpoint source, has been known with high uncertainty. In this study, a new industrial nonpoint source (NPS) reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for emission development in China. The new method utilized the zoom level 14 satellite imagery (i.e., Google®) and processed it with Hue, Saturation, Value (HSV)-based colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions. The WRF-CMAQ based PATH-2016 model system was then applied with the new NPS emissions processed emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta (PRD)). In the study, significant RMSE improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e., 76 stations) of PM2.5 and NO2 were found to be 11 μg/m3 and 3 ppb, respectively. This research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial NPS emissions from regional to local levels (i.e., 27 km to 3 km resolution), overcoming the technical hurdle of ~ 10 km resolution from the top-down emission approach under the unified framework of emission calculation.

Although the effects of meteorological factors on severe air pollution have been extensively investigated, quantitative decomposition of the contributions of meteorology and anthropogenic factors remains a big challenge. The novel coronavirus disease 2019 (COVID-19) pandemic affords a unique opportunity to test decomposition method. Based on a wind decomposition method, this study outlined an improved method to differentiate complex meteorological and anthropogenic effects. The improved method was then applied to investigate the cause of unanticipated haze pollution in China during the COVID-19 lockdown period. Results from the wind decomposition method show that weakened winds increased PM2.5 concentrations in the Beijing–Tianjin area and northeastern China (e.g., by 3.19 μg/m3 in Beijing). Using the improved decomposition method, we found that the combined meteorological effect (e.g., drastically elevated humidity levels and weakened airflow) substantially increased PM2.5 concentrations in northern China: the most substantial increases were in the Beijing–Tianjin–Hebei region (e.g., by 26.79 μg/m3 in Beijing). On excluding the meteorological effects, PM2.5 concentrations substantially decreased across China (e.g., by 21.84 μg/m3 in Beijing), evidencing that the strict restrictions on human activities indeed decreased PM2.5 concentrations. The unfavorable meteorological conditions, however, overwhelmed the beneficial effects of emission reduction, causing the severe haze pollution. These results indicate that the integrated meteorological effects should be considered to differentiate the meteorological and anthropogenic effects on severe air pollution.

Low-cost air quality sensors for air pollution monitoring have shown huge potential in enhancing the spatial and temporal resolution of much needed pollution data at a lower cost, greater flexibility in use with less maintenance than air quality monitoring stations. Air quality sensors provide opportunities to extend a range of existing air pollution monitoring capabilities and provide ideas for new monitoring applications. The widespread data sources combined with sensors improve people’s understanding of air quality research areas. However, severe gaps existed between the guarantee of sensor data quality and derivation of meaningful information from sensor applications in air quality monitoring. This study provides avenues from sensor calibration to applications in urban air quality monitoring. Three steps were divided as follows: Firstly, the air quality sensors were tested in the laboratory-controlled environment to understand the sensor performance and influence factors. The ambient factor influence was identified and quantified to build correction algorithms. Based on the understanding of the sensor working principle, a principle-based correction algorithm was built. The new algorithm proves the sensor sensitivity variation trend during different seasons. Meanwhile, compared with commonly used correction models, it shows the best performance and spatial representativeness in the sensor network. Secondly, a mobile sensor network was deployed in Hong Kong to monitor traffic-related air pollutants (TRAP) along bus routes. To estimate local and background contributions, a robust baseline extraction algorithm was developed and evaluated. The result indicates NO and NO2 are locally dominated air pollutants and varied within roads. Background concentrations primarily arose from CO and PM2.5 and decreased during summertime. The regional transport pollution is the primary contributor during high pollution episodes. For the traffic induced local co

Air pollution is a leading environmental risk factor for premature death globally. People are typically exposed to gaseous and particulate pollutants simultaneously in the real-world. People spend various time in different microenvironments, including home, office, transit, others and outdoor. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as toxicity of pollutants. Most of the existing individual exposure studies were conducted on particulate matter (PM). Limited information is available on personal exposure variability for gaseous pollutants (i.e., NO2 and O3) due to complexity and difficulty in measuring those pollutants. Using added health risk (AR) model, we evaluated short-term health risk of NO2, O3 and PM2.5 based on ambient concentrations in urban areas with dense traffic and less urbanized areas. Although PM2.5 has a significant long-term health risk, NO2 and O3 are more predominant in short-term health risk than PM2.5. Thus, with the recent technological advancement, we measured real-world personal exposure to both gaseous and particulate pollutants using next generation sensors across 21 participants in their daily life. We quantified health risk of combined exposure to NO2, O3 and PM2.5 using AR model. Inter-and intra-individual variability in health risks and sources of variations were investigated using linear mixed-effects models and correlation analysis, respectively. Daily time-integrated AR for combined NO2, O3 and PM2.5 (TIARcombine) ranged by a factor of 2.5 among participants and by a factor of 1.0 to 2.5 for a given person across measured days. Several factors were identified to be significantly correlated with daily TIARcombine, with the top 5 factors including N

Shipping activities contribute to degraded air quality and premature mortalities worldwide, but previous assessments of their health impact have not yet differentiated contributions from domestic and international shipping at the global level. The impacts of domestic shipping can affect different populations, and domestic and international shipping emissions are governed under different regulatory systems. Thus, a consistent global analysis comparing the health impacts from domestic and international shipping could inform policy making in attempts to coordinate policies across multiple scales to address the health burden of shipping emissions. In this study, we create bottom-up global ship emission inventories based on ship activity records from the automatic identification system, and then apply the GEOS-Chem atmospheric model and global exposure mortality model to quanitfy shipping-related PM2.5-concentrations and associated mortalities. We also quantify the public health benefits under different control scenarios including the 2020 0.5% sulphur cap, a post-2020 0.1% sulphur cap, and a post-2020 Tier III NO (x) standard. We find that 94 200 (95% confidence interval: 84 800-103 000) premature deaths were associated with PM2.5 exposure due to maritime shipping in 2015, of which 83% were associated with international shipping activities and 17% with domestic shipping. Although the global health burdens of ship emissions are dominated by international shipping, the fraction varies by region: 44% of shipping-related premature deaths in China come from domestic shipping activities. We estimate about 30 200 (27 200-33 000) avoided premature deaths per year under a scenario consistent with a 2020 0.5% sulphur cap. We find that a post-2020 Tier III NO (x) standard would have greater benefits than a post-2020 0.1% sulphur cap, with the two policies reducing annual shipping-attributable PM2.5-related premature deaths by 33 300 (30 100-36 400) and 5070 (4560-5540), respective

An initial condition that closely represents the true atmospheric state can minimize errors that propagate into the future, and could theoretically lead to improvements in the forecast. This study aims to evaluate and understand the impacts of 3DVAR on the state-of-the-art Weather Research and Forecasting (WRF) model with a two nested domains setup. The domain configuration of the model covers China with an emphasis on Guangdong province, with a resolution of 27 km, 9 km, and 3 km. Improvements in the forecasts for the Winter and Summer season of all the domains are systematically compared and are quantified in terms of 2 m temperature, 10 m wind speed, sea level pressure, and 2 m relative humidity. The results show that 3DVAR provides significant improvements in the winter case and surprisingly improvements were also found after the 48 h of the forecast. Evaluations of performance of 3DVAR in different domains and between two different seasons were done to further understand the reasons behind the discrepancies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Long-term ozone lidar routine measurements are sparse in China. In this study, we used one year of continuous ozone lidar measurements to systematically investigate the effects of synoptic patterns on the vertical structure of ozone during high ozone episodes in Hong Kong. The height of the near-surface ozone layer was identified and compared with that of the mixing layer. The results showed that Hong Kong was greatly affected by ozone transport from the Pearl River Delta region when a tropical cyclone (“C” pattern) or a trough (“T” pattern) existed. When a high-pressure center was located to the north (“H” pattern) or when both a high-pressure system and a tropical cyclone existed (“CH” pattern), Hong Kong was affected by ozone transport from southeastern or southern China. Substantial amounts of ozone were transported within a layer near the ground with an average height of 0.83, 0.75, 0.94, and 0.85 km under the C, H, CH, and T synoptic patterns, respectively. Variation in the near-surface ozone layer height was consistent with that of the mixing layer height. Weak vertical mixing resulted in a shallow near-surface ozone layer under the C and H synoptic patterns. Due to the combined effect of near-surface transport and suppressed vertical mixing, the ozone concentrations were extremely high near the ground but rapidly declined with height under the C synoptic pattern. Our investigations of the vertical structure of ozone contribute to filling the knowledge gap between synoptic patterns and the evolution of high surface ozone episodes.

The interdecadal changes in the diurnal cycle of summertime rainfall over coastal South China (CSC) were analyzed for the period of 1884–2018. The results revealed that rainfall intensity has increased with the decrease of occurrence frequency during both pre‐ and mid‐summer in the past 135 years. During the most recent 40 years, occurrence frequency and intensity of nocturnal rainfall, especially extreme rainfall, have substantially increased over CSC, whereas those of daytime extreme rainfall have decreased. Different increasing rates of near‐surface temperature over land and sea are the main contributors to the distinct trends of daytime and nocturnal extreme rainfall. During daytime, a higher increasing rate of near‐surface temperature over sea results in a smaller temperature gradient along the coast, leading to a less convective atmosphere. This negates the effect of increased air‐temperature over CSC in both pre‐ and mid‐summer. In contrast, during nighttime, a higher increasing rate of near‐surface temperature over sea results in a greater temperature gradient along the coast, leading to stronger low‐level offshore winds and more convective layers over coastal waters. During pre‐summer, the stronger nocturnal low‐level offshore winds meets the warm and moist monsoonal flows, substantially enhancing nocturnal extreme rainfall along the coast. In mid‐summer, more vigorous upward motions over coastal waters of CSC enhance convective activities during nighttime, which also lead to an intensification of nocturnal rainfall. The most significant increase and intensification of extreme rainfall is observed in western CSC during pre‐summer, where the land‐sea difference of near‐surface temperature is the greatest over CSC.

As one of the largest agglomerations of cities in the world, the infrastructure and living conditions of the residents in the Pearl River Delta (PRD) have significantly improved since the late 1970s. However, as a two-sided sword, the boosting of economic growth has inevitably exerted adverse effects on the environment in this region. To further understand the current fine particulate matter (PM2.5) pollution sources in this region and formulate an effective future air pollution control strategy for it, a hybrid source apportionment method was applied to adjust source estimates of secondary inorganic aerosols (SIAs) [particulate sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+)] by combining Comprehensive Air Quality Model Extensions and measurement data from 19 sampling sites covering the PRD region in 2015. After correction by the hybrid method, the normalized mean errors for the three major species SO42−, NO3−, and NH4+ in PM2.5 simulated by the model decreased from 0.412, 1.261, and 0.401 to 0.321, 0.565, and 0.130, respectively. Tianhu, Taishan, Taipa Grande, whose secondary inorganic aerosol concentration variations are −48.33%, −40.59%, and 39.10% after adjustment, respectively, are the three top sites with the most substantial modification. Our study revealed that the emissions outside the PRD region are still the most important contributor to the three components after the correction. In most Hong Kong sites, the adjusted mobile source is subordinate to the cross-boundary transport and became the largest endogenous source. In contrast to that in Hong Kong, preceded only by super-regional sources, the outstanding contributor to SIAs in Guangdong and Macao is the area source. Our results indicated that besides collaborative control measures, the Hong Kong and Guangdong government should emphasize the emissions from motor vehicles and residential sources, respectively. Overall, this hybrid source apportionment approach can narrow down the discrepancy betwee

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM2.5 and other ambient pollutants.

The spatiotemporal concentration of multiple pollutants is crucial information for pollution control strategies to safeguard public health. Despite considerable efforts, however, significant uncertainty remains. In this study, a three‐dimensional variational model is coupled with a data assimilation (DA) system to analyze the spatiotemporal variation of PM2.5 for the whole of China. Monthly simulations of six sensitivity scenarios in different seasons, including different assimilation cycles, are carried out to assess the impact of the assimilation frequency on the PM2.5 simulations and the model simulation accuracy afforded by DA. The results show that the coupled system provides more reliable initial fields to substantially improve the model performance for PM2.5, PM10, and O3. Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM2.5 mass concentrations in the analysis field—are improved by 12.19 µg/m3 (33%) and 0.21 (48%), respectively. Although the 24‐h assimilation cycle considerably improves the model, assimilation at a 6‐h cycle raises the performance for PM2.5 to the performance goal level. The analysis shows that assimilating at a 24‐h cycle diminishes over time, whereas the positive impact of the 6‐h cycle persists. One pivotal finding is that the assimilation of PM2.5 in the outermost domain results in a substantial improvement in PM2.5 prediction for the innermost domain, which is a potential alternative method to the existing domain‐wide data fusion algorithm. The effect of assimilation varies among topographies, a finding that provides essential support for further model development.

O3 pollution had been worsening in mainland China in the past decade, posing significant human health challenges. The NOx control would trigger increasing O3 concentrations in response to a series of released China emission reduction policies. This study used sensitivity analysis methodology to explore the effectiveness of integrated sectoral emission control policies that have been expanded throughout China. Air quality and synergistic health effects of O3 and NO2 were investigated to obtain an in-depth understanding of the O3 control, especially under a VOC-limited regime. The findings demonstrated that although the NOx-titration effect triggered an increase in O3, the combined health effects of two pollutants tended to improve in most regions of China under a VOC-limited regime. The region-based annual average NO2 concentrations exhibited a larger reduction in Hong Kong (HK) than in the Pearl River Delta Economic Zone (PRD EZ). The short-term measures led to substantial health benefits for Shenzhen and HK. The sectoral emission controls demonstrated a considerable health improvement for the major PRD EZ cities. Joint national control efforts confined the domain-wide health risks below the safety line in China. National cooperative efforts in China could avoid more than 1.5–2% of the emergency hospital admissions for cardiovascular and respiratory diseases attributed to NO2 and O3 exposure. The observed O3 increases due to the NOx-titration effect for calculating the integral health effects of emission control on concentration reduction called for simultaneously strengthened controls on both NOx and VOC in areas subject to a VOC-limited regime