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Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations. Significance Statement Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

Previous studies on South China’s convective precipitation forecast focused on the effects of multi-scale dynamics and microphysics parameterizations. However, how the uncertainty in aerosol data might cause errors in quantitative precipitation forecast (QPF) has yet to be investigated. In this case study, we estimate the impact of aerosol uncertainties on the QPF for South China’s severe convection using convection-permitting simulations. The variability range of aerosol concentrations is estimated with past observation for the pre-summer months. Simulation results suggest that the rainfall pattern and intensity change notably when aerosol concentrations are varied. The simulation with low aerosol concentrations produces the most intense precipitation, approximately 50\% stronger than the high-concentration simulation. Decreasing aerosol hygroscopicity also increases precipitation intensity, especially in pristine clouds. The aerosol uncertainty changes alter the number of cloud condensation and ice nuclei, which modifies the altitude and amount of latent heating and thereby modulates convection.

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, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial 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 root mean square error (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. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, 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 emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.

Wet deposition of non-sea-salt sulfate (nss-SO42-) and nitrate (NO3-), derived from anthropogenic emissions of SO2 and NOx, exerts adverse effects on ecosystems. In this work, an ensemble back-prop...

The below-cloud washout (BCW) effect on PM2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h−1 to 60 mm h−1. The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10−5 to 10−4 s−1 when the rain intensity ranges from 1 to 40 mm h−1. After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM2.5 simulation during rainy days. The BCW effect can clean around 10–40% of the PM2.5 over our study region, which can help to reduce the PM2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM2.5 concentration, but it cannot be the key method to reduce the PM2.5 level to the standard during episodic cases (e.g., >200 μg/m3).

During China's 11th Five Year Plan (FYP) and 12th FYP (2006–2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 μg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 μg/m3 in 2006 to 2.6, 3.1 and 4.1 μg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 μg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

The Pearl River delta (PRD) region has undergone rapid urbanization since the 1980s, which has had significant effects on the sea-breeze circulation in this region. Because the sea breeze plays an important role in pollutant transportation and convective initiation in the PRD region, it is meaningful to study the effects of urbanization on the sea breeze. In this study, three numerical experiments were conducted from 2 June to 31 August 2010 with land-use data from 1988, 1999, and 2010. For each simulation, characteristics of the sea breeze such as the start time, end time, intensity, height, pumping ability, and inland penetration distance were quantified. By comparing the characteristics of the sea breeze in these simulations, its response to urbanization was quantified. The results show that urbanization enhances the duration, height, and intensity of the sea breeze but blocks its inland penetration. One physical mechanism is proposed to dynamically elucidate the response of the sea breeze to urbanization. Because the urban area in the PRD region is concentrated near the coast, urbanization imposes a positive heating gradient on the coastal region and a negative heating gradient on the region farther inland. The positive heating gradient may intensify the sea breeze, and the negative heating gradient may prevent the sea breeze from propagating farther inland.

The Pearl River delta (PRD) region has experienced rapid economic development since the 1980s and has become one of the world’s largest industrial zones and metropolitan areas. Previous studies have shown that the sea-breeze circulation can contribute to pollutant transportation and convective initiation, so it is useful to study the dynamic structure of the sea-breeze circulation in the PRD region. Many researchers have focused on the effects of environmental factors, such as topography, urbanization, and background wind, on the sea breeze, but most focused only on case studies and did not quantify the characteristics of the sea-breeze circulation climatologically. In this study, a sea-breeze identification metric was defined to identify sea-breeze events from WRF simulation data of 2012 and quantify their characteristics, including their start time, end time, strength, height, frequency, pumping ability, and inland-penetrating distance. The results indicate that this method works well to identify and quantify the sea-breeze events of 2012. It is found that the solenoid term, the largest positive contributor to vorticity acceleration, is mostly modulated by the temperature gradient. Therefore, the frontogenesis of the sea-breeze front is discussed in this study. The result shows that offshore background wind that increases frontogenesis is favorable to the development of the sea breeze, but it also prevents it from propagating vertically and horizontally.

Automatic Identification System (AIS) data have been widely used to estimate ship emission inventories. However, although the AIS dataset can provide high spatial and temporal ship activities, the estimation of ship emission inventory requires vessel information that may not always be available in the vessel parameter database (e.g. engine power). Hence, earlier AIS-based emission inventory studies mainly included vessels with engine specifications (referred to as identified vessels); the significance of emissions from vessels without engine or other pertinent specifications (referred to as unidentified vessels) has not been studied in detail. In this study, we develop an emission inventory of all vessels with installed AIS instruments, including both identified and unidentified vessels. In particular, missing ship parameters for unidentified vessels are estimated through categorical regression with vessels with similar ship types and sizes in the AIS database. Using this approach, we estimate the ship emission inventory for the Pearl River Delta (PRD) in 2015. Our results show that unidentified vessels contributed significantly, accounting for almost 49% of the overall ship emissions of CO2, NOx, CO, and hydrocarbon in the PRD. Overall, we find that identified vessels contributed more over the open ocean and near port areas, whereas unidentified vessels contributed significantly along inland waterways with clear emission tracks and hot spots.

Buildings or fence-like structures are frequently modelled as cuboids in simulations of environmental assessments. Understanding flows around and downwind of typical isolated buildings, in the form of cuboids, provide insightful building disposition strategies, which are particularly useful in the field of urban planning. Fast analytical models or computational numerical models are essential for sensitivity studies of various design parameters that require validation against field/experimental data. The aim of this study is to compare in detail the robustness of analytical perturbation models and the computational fluid dynamics (CFD) k − ε turbulence models for predicting the mean wake velocity defects along the centre line downwind of the cuboids, both in the near- and far-wake regions. Depending on aspect ratios of cuboids, the decay rate of maximum velocity defect behaves differently. Moreover, the CFD code over-predicts the magnitude of maximum defect in comparison with the perturbation models, whereas it over-predicts or under-predicts when compared to actual measurements. A physical explanation has been proposed in this paper. The steady CFD models, requiring less empirical input, are shown to provide satisfactory results for approximate computations of flows in the near- and far-wake regions of cuboid buildings with appropriate settings of inflow profiles and wall function.

Variations in aerosol characteristics play an essential role in satellite remote sensing of PM2.5 concentrations. The lack of measurement of aerosol characteristics, however, limits the assessment of their effects. This study presented an observation-based model that directly considered the effects of aerosol characteristics. In this model, we used an integrated humidity coefficient (γ') and an integrated reference value ( K) to delineate the effects of aerosol characteristics. We then investigated the effects of the long-term variations in aerosol characteristics on satellite remote sensing of PM2.5 concentration in Hong Kong from 2004 to 2012. The results show that the γ' value peaked in 2009 because the percentages of highly hygroscopic components (e.g., sulfate and nitrate) in aerosols reached their peaks. The K value increased from 2004 to 2011 because of the increasing percentages of strong light-extinction components (e.g., organic matter) and the decreasing fine mode fraction in aerosols. The accuracy of PM2.5 retrieval improved greatly after accounting for the long-term variations in aerosol characteristics (e.g., correlation coefficient increased from 0.56 to 0.80). The results underscore the need to incorporate the variations in aerosol characteristics in the PM2.5 estimation models.

The health effects of atmospheric particulate matter (PM) have become a major environmental concern in urban areas. Most PM studies are mainly designed to measure the “ambient” or “emitted” concentrations of PM. Some studies are specifically designed to address exposure to PM for pedestrians and/or commuters on-board vehicles or at bus stops, but less attention is paid to the exposure during physical exercise such as jogging. To this end, concentrations of both fine particulate matter (PM2.5) and black carbon (BC) were measured along three jogging trails in the densely populated city Macau in China. The three jogging trails include the campus of University of Macau (UM), Guia Municipal Park (GP), and Saivan Lake (SL). In our measurements, PM2.5 and BC ranged from 2.9 to 84.1 and 0.4–19.5 μg/m³, respectively. BC/PM2.5 ratio ranged from 0.016 to 0.448. Among all three jogging trails, the highest BC concentration was found at SL (19.5 μg/m³), and the highest PM2.5 concentration was found at UM (84.1 μg/m³). On the contrary, the BC and PM2.5 concentrations at the elevated (about 50 m above sea level) GP trail were lower than those at the other two jogging trails. BC and PM2.5 concentrations were generally lower in the night loops (21:30–23:00) than those in the morning loops (07:30–09:00), which coincide with morning rush hours, with only a few exceptions. The difference in geographical locations also affects the BC and PM2.5 concentrations measured, with locations near bus terminals, busy roads, or with congested street canyons having higher concentrations. Doses of BC and PM2.5 after 60 min of exposure during typical jogging exercise are also estimated to evaluate the exposure to PM pollution at these three jogging trails when exercising. The results from the current studies provide information both on personal choice for the time/venue for jogging exercise and on future abatement policy to mitigate such risks of exposure to BC and PM2.5.

In this study, the trend of PM2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM2.5 data. PM2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM2.5 concentration and mortality are analyzed. The PM2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.

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.

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities.

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, −0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between −9.41 °C and −13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (, Graphical abstract Unlabelled Image, Highlights • A mechanism-based parameterisation scheme was developed for transmission rate. • Scale of population migration from Wuhan was incorporated. • Population density had a non-linear (linearity index, 0.9) impact on disease spread. • Transmission rate shows a negative and exponential temperature dependence. • COVID-19 mitigation in densely populated and cold regions will be a great challenge.

The coronavirus disease 2019 (COVID-19) pandemic has killed over 0.3 million people, disrupted people’s normal lives, and severely restricted economic activities globally. In this work, a model for the next-day COVID-19 prediction in China was built based on the ensemble back-propagation neural network machine learning technique, Baidu migration index, internal travel flow index, and confirmed cases from the previous days. The 10-fold cross-validation results showed that the model performs well in estimating the next-day confirmed cases with a correlation coefficient of 0.97. To investigate the impacts of government interventions on the spread of this new coronavirus infection, the Baidu migration index and internal travel flow index multiplied by a factor of two were input into the trained machine learning model, and the results showed that the confirmed cases in the analyzed cities would increase dramatically. The correlation between the daily new confirmed cases and some meteorological factors were also analyzed, and the results revealed that these factors are not dominant in influencing the spread of this disease. Overall, the results of this work suggest that besides early diagnosis and medical treatment, a city lockdown policy is one of the most effective methods in suppressing the rapid spread of COVID-19.

Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.

Urbanization is one of the most significant contributing factors to anthropogenic climate change. However, a lack of projected city land use data has posed significant challenges to factoring urbanization into climate change modeling. Thus, the results from current models may contain considerable errors in estimating future climate scenarios. The Pearl River Delta region was selected as a case study to provide insight into how large-scale urbanization and different climate change scenarios impact the local climate. This study adopts projected land use data from freely available satellite imagery and applies dynamic simulation land use results to the Weather Research and Forecasting Model (WRF). The simulation periods cover the summer periods in 2010 and 2029&ndash, 2031, the latter of which is averaged to represent the year 2030. The WRF simulation used the observed local climate conditions in 2010 to represent the current scenario and the projected local climate changes for 2030 as the future scenario. Under all three future climate change scenarios, the warming trend is prominent (around 1&ndash, 2 &deg, C increase), with a widespread reduction in wind speed in inland areas (1&ndash, 2 ms&minus, 1). The vulnerability of human health to thermal stress was evaluated by adopting the wet-bulb globe temperature (WBGT). The results from the future scenarios suggest a high public health risk due to rising temperatures in the future. This study provides a methodology for a more comprehensive understanding of future urbanization and its impact on regional climate by using freely available satellite images and WRF simulation tools. The simulated temperature and WBGT results can serve local governments and stakeholders in city planning and the creation of action plans that will reduce the potential vulnerability of human health to excessive heat.

Air pollutants emitted within high-density urbanized areas have negative impacts on human health and quality of life and raise public concern. With the aim of creating a liveable environment and mitigating potential environmental problems, environmental scientists, architects and town planners have been working together to discover optimum building layouts and guidance principles for building morphology. These efforts have been consolidated via model simulations. Targeting an optimal design strategy, an improved SST k − ω model was first validated against wind and pollutant concentration data from a wind tunnel experiment at Tokyo Polytechnic University. The turbulence model was then applied to a variety of idealized city morphologies with different building permeabilities and building-height profiles. To confirm the findings from these idealized cases, further simulations were performed on two real conceptual cities. To facilitate pollutant dispersion within urbanized areas, at least 20% of the building permeability should be maintained, together with variations in building-height profiles. The innovative “Dragon Hole” design was also found to aid in pollutant dispersion. The study findings show that the pollutant concentration within the source canyon would be reduced by at least 40% for the idealized cases and by approximately 80% for the conceptual real case through incorporation of the abovementioned design features. Moreover, the recommendations resulting from this research are consistent with the requirements documented in the Sustainable Building Design Guidelines.

The Pearl River Delta (PRD) region is one of the most developed city clusters in China, and it is also the area that suffers from severe air pollution. A key problem in addressing pollution is to find out where the pollutants come and how to control them. Most of the previous studies focused on the source area, and source category contribution analysis, but fewer studies paid attention to the temporal contribution, which is also an important factor in policymaking. Therefore, in this study, based on the CAMx-PSAT model, we extended the model to track the contribution of the sources emitted at different periods. The updated PSAT can reflect the temporal correlation between the source and receptor and provide scientific support to efficient control policymaking. The simulation result of a high PM2.5 episode shows that the emission outside the PRD region is the major contributor to PM2.5 over the PRD region. PM2.5 mainly comes from the emission within the current two days. Under the control of the high-pressure system, low wind speed hinders the diffusion of PM2.5 and paves the way for the accumulation of the pollutants. The emission two days ago can still have a considerable contribution during the high concentration period. The results suggest that emission control measurements should be implemented in advance when adverse meteorology condition is predicted.

11 pags., 7 figs., Recent studies have focused on the chemistry of tropospheric halogen species which are able to deplete tropospheric ozone (O). In this study, the effect of bromine and iodine chemistry on tropospheric O within the annual cycle in Asia-Pacific is investigated using the CMAQ model with the newly embedded bromine and iodine chemistry and a blended and customized emission inventory considering marine halogen emission. Results indicate that the vertical profiles of bromine and iodine species show distinct features over land/ocean and daytime/nighttime, related to natural and anthropogenic emission distributions and photochemical reactions. The halogen-mediated O loss has a strong seasonal cycle, and reaches a maximum of −15.9 ppbv (−44.3%) over the ocean and −13.4 ppbv (−38.9%) over continental Asia among the four seasons. Changes in solar radiation, dominant wind direction, and nearshore chlorophyll-a accumulation all contribute to these seasonal differences. Based on the distances to the nearest coastline, the onshore and offshore features of tropospheric O loss caused by bromine and iodine chemistry are studied. Across a coastline-centric 400-km-wide belt from onshore to offshore, averaged maximum gradient of O loss reaches 1.1 ppbv/100 km at surface level, while planetary boundary layer (PBL) column mean of O loss is more moderate, being approximately 0.7 ppbv/100 km. Relative high halogen can be found over Tibetan Plateau (TP) and the largest O loss (approximately 4–5 ppbv) in the PBL can be found between the western boundary of the domain and the TP. Halogens originating from marine sources can potentially affect O concentration transported from the stratosphere over the TP region. As part of efforts to improve our understanding of the effect of bromine and iodine chemistry on tropospheric O, we call for more models and monitoring studies on halogen chemistry and be considered further in air pollution prevention and control policy., This work was supported by the Air Pollution Control Program of the National Key Research & Development Plan (2018YFC0213902), Research Grants Council of the Hong Kong Government (Project No. R6011-18, R4046-18, T24/504/17), Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (Grant No.2019B121205004), and the European Research Council Executive Agency under the European Union’s Horizon 2020 Research and Innovation Programme (Project ERC-2016- COG 726349 CLIMAHAL).

Along with rapid economic development in China in the past twenty years, air pollution has deteriorated significantly and then gradually improved afterwards. Emission changes in response to the economic development and control measures and their impacts to ambient pollution levels are thus of great scientific and policy-making implications. By using the eighteen-year PM10 speciation dataset that have been continuously collected at multiple stations over Hong Kong since 1998, we demonstrated a novel approach to identify long-term trends of local and non-local contributions to ambient PM10 in Hong Kong based on Positive Matrix Factorization source apportionment and backward trajectory cluster analysis. It is found that vehicle exhaust contribution had decreased from 9.9 μg·m−3 in 1998 to 3.4 μg·m−3 in 2015, corresponding to a 66% decrease. However, residual oil contribution increased threefold, which indicated ship emission needs to be primarily addressed in air pollution management in Hong Kong. The mostly non-local sources of secondary sulfate, industry, and coal / biomass burning all showed inverse V-shape trends during the eighteen years, indicating the dual roles of economic development and emission control in shaping the ambient pollution levels. Local contributions dominated for vehicle exhaust and aged sea salt, while non-local ones dominated for secondary sulfate, secondary nitrate, industry, and coal / biomass burning. Although with small fluctuations, non-local contributions consistently accounted for 63–70% of total PM10 during the eighteen years, similar as previous estimates using a different approach. Such a dominant non-local contribution highlights the importance of regional collaboration to reduce PM levels in Hong Kong.

This paper investigates the evolution of the climatological effects of urbanization in the major cities of the Pearl River Delta region of China during the summer season. Land use data representing the 1990s, 2000s, and 2010s are obtained by classifying the land use from collected Landsat images. This classification standard follows the guidelines of land use classification from the World Urban Database and Access Portal Tools (WUDAPT). Before the model simulation, the WUDAPT land use categorization was remapped according to the United States Geological Survey (USGS) land use classification. The Weather Research and Forecasting (WRF) model was then applied under the same initial and boundary conditions with respect to different land use data. Spatial comparison and statistical analysis reveal a general increase in temperature (approximately 1 °C) and heat index (2 °C at night) and a deceleration of wind speed over time (around 0.5 ms−1) when compared with the 1990s. These impacts are due to urbanization. Moreover, simulation shows that the sensible heat flux is increased, whereas the latent heat flux is decreased because there was less vegetation and more impervious surfaces. These findings can let planners and governors have a quantitative understanding about the impact of urbanization on local climatic conditions.

Mobile air quality monitoring reports air pollutant concentrations at a high spatiotemporal resolution, enabling the characterization of heterogeneous human exposure and localized pollution hotspots. In this study, on-road concentrations of fine particulate matter (PM2.5) in a high-density urban area in Hong Kong were measured in December 2014 and January 2015 (winter) and June and July 2015 (summer) using a tramcar mobile monitoring platform. We developed a method of mapping the winter and summer on-road PM2.5 concentrations along a tramcar route at a 50-m spatial resolution, using mobile measurements. In addition, the minimum number of days required to precisely estimate on-road PM2.5 concentrations was estimated. The results showed that the on-road PM2.5 concentrations were highly correlated with PM2.5 concentrations measured at a nearby roadside air quality monitoring station (AQMS) in both winter and summer, with Pearson correlation coefficients of 0.89–0.93. The resulting maps of winter and summer on-road PM2.5 concentrations revealed small-scale spatial patterns used to identify more polluted areas. In addition, approximately 12 and 4 days were required to precisely capture spatial patterns of PM2.5 concentrations, with R2 higher than 0.6 in winter and in summer. The findings of this study offer valuable information on air pollution control and exposure reduction by highlighting localized pollution hotspots, and provide insights into the minimum sampling duration for mobile sampling campaigns.

Given the vast territory of China, the long-term PM2.5 trends may substantially differ among the provinces. In this study, we aim to assess the provincial PM2.5 trends in China during the past few Five-Year Plan (FYP) periods. The lack of long-term PM2.5 measurements, however, makes such assessment difficult. Satellite remote sensing of PM2.5 concentration is an important step toward filling this data gap. In this study, a PM2.5 data set was built over China at a resolution of 1 km from 2001 to 2015 using satellite remote sensing. Analyses show that the national average of PM2.5 concentration increased by 0.04 μg·m−3·yr−1 during the 10th FYP period (2001–2005) and started to decline by −0.65 μg·m−3·yr−1 and −2.33 μg·m−3·yr−1 during the 11th (2006–2010) and the 12th (2011–2015) FYP period, respectively. In addition, substantial differences in the PM2.5 trends were observed among the provinces. Provinces in the Beijing-Tianjin-Hebei (BTH) region had the largest reduction of PM2.5 concentrations during the 10th and 12th FYP period. The greatest reduction rate of PM2.5 concentration during the 10th and 12th FYP period was observed in Beijing (−3.68 μg·m−3·yr−1) and Tianjin (−6.62 μg·m−3·yr−1), respectively. In contrast, PM2.5 concentrations remained steady for provinces in eastern and southeastern China (e.g., Shanghai) during the 12th FYP period. In overall, great efforts are still required to effectively reduce the PM2.5 concentrations in future.

The adverse impact of climate change-associated extreme weather events is becoming more significant globally, particularly the flood impact on coastal and low-lying areas such as the Pearl River Delta (PRD). This study applied the framework to obtain order-of-magnitude estimations of human damages from future flood disasters caused by sea level rise for Hong Kong and the PRD region in southern China by 2050 and 2100. The assessment framework employs statistical analysis to combine global historical flood damage data with national development indicators and local sea level characteristics to assess the potential damages. Following the terminology of the Intergovernmental Panel on Climate Change Special Report on Extreme Events, the three determinants of disaster risk (climate extreme, exposure and vulnerability) are quantified in our framework. It is found that without adaptation, sea level rise will significantly increase the flood risk in this region. For instance, in the PRD region, with a 75-cm sea level rise by 2100, the deaths and displacements from a 100-year flood are estimated to be around 200 and 1.5 million, respectively. Our results provide motivation for regional authorities to adopt a long-term adaptation plan to reduce exposure and vulnerability to flooding, thus managing the risks in this region. Furthermore, with appropriate datasets available, our framework allows the assessment of the effects of flooding in other areas and/or the quantitative evaluation of potential losses from other climate-related hazards such as heat waves.

In the modern era, people spend approximately 90% of their time in indoor settings, such as offices and residential buildings. As prolonged exposure to indoor environments can significantly impact health outcomes, it is important to ensure that good indoor air quality (IAQ) is achieved. The main obstacles to achieving effective control of IAQ are twofold. First, it is very difficult to monitor the values of IAQ parameters, especially within a person's breathing zone. Second, current heating ventilation and air conditioning systems are unable to rapidly predict and optimize IAQ. This study aims to obtain accurate indoor environmental parameters for rapidly predicting and optimizing IAQ. To achieve this, a computational fluid dynamics (CFD)-based back propagation neural network (BPNN) combined with a particle swarm optimizer (PSO) algorithm is proposed. Notably, the BPNN-PSO algorithm can rapidly predict and optimize IAQ while using a limited number of CFD runs. In comparison with other state-of-the-art methods for controlling IAQ, such as the artificial neural network (ANN)-genetic algorithm (GA), the BPNN-PSO algorithm further reduces the concentration of indoor air pollutants by up to 6.44% and computational costs by as much as 23.53%. The use of CFD ensures that the information obtained is accurate and allows for rapid prediction of indoor environmental conditions. The BPNN-PSO algorithm has the potential to contribute very effective and intelligent control strategies for ventilating indoor environments.

Heating ventilation and air conditioning (HVAC) systems provide a comfortable indoor thermal environment, but in the process of attaining appropriate indoor thermal comfort levels, they usually entail high energy consumptions. It is therefore imperative to balance thermal comfort value with energy consumption. However, such research currently faces two problems: one, it is difficult to obtain accurate parameters pertaining to the indoor environment of buildings, particularly near heat source areas; two, it is the diametrical nature of having to simultaneously maintain thermal comfort and keep energy consumption low. Therefore, this study aims to propose a rapid thermal comfort level prediction and optimization algorithm, as well as a method to minimize the energy consumption using only a computational fluid dynamic (CFD) database that is compact in size. Firstly, CFD is used to implement the database that stores data on indoor airflow and temperature distributions of different building structures and indoor conditions. Next, using the database as a basis, a back-propagation neural network (BPNN) is developed to predict the thermal comfort level. The adaptive grey wolf optimizer (GWO) algorithm is then applied to optimize the thermal comfort value, and the latest control methods: the artificial neural network (ANN)-genetic algorithm (GA) and ANN-particle swarm optimizer (PSO) algorithm are compared against the BPNN-based adaptive GWO method. The results show that the BPNN-based adaptive GWO algorithm can rapidly predict the thermal comfort level and have strong optimization ability. Meanwhile, 1.01% of energy savings are achieved.

Air pollution is a major impediment to the sustainable development of cities and society. Governed by emission characteristics and meteorological conditions, the formation and destruction of fine particulate matter (PM2.5) and ozone (O3) are complicated, and accurate predictions of the concentrations of these two major secondary atmospheric pollutants remain challenging. In this study, by combining meteorological and air pollutant data from ground observations and the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations, a deep learning model structure based on long short-term memory layers (LSTM) was developed and applied to predict the PM2.5 and O3 concentrations in the future 48 h period. The forecasting improvement was extended to the whole Greater Bay Area by introducing a spatial correction (SC) method to the CMAQ simulation results. Compared with the original CMAQ forecast, the new method gained a 26% reduction in mean absolute error (MAE) and a 33% reduction in root mean square error (RMSE), respectively, in terms of PM2.5; it also achieved a 40% reduction in MAE and a 34% reduction in RMSE in terms of O3. SC method, applied to the whole GBA region, also reduced the overall MAE and RMSE by 10% and 17% in terms of PM2.5 and by 31% and 25% in terms of O3, respectively. Using an AI approach, our study provides new perspectives for further improving air quality forecasting from both temporal and spatial perspectives, thus increasing the smartness and resilience of the cities and promoting environmentally sustainable development in the area.

Rapid economic development and urbanization have caused severe pollution from PM2.5 in the Pearl River Delta (PRD) region of China. Characterizing the long-term PM2.5 trends is a basic requirement for evaluating the effectiveness of the implemented control measures and for guiding future policies. Traditional characterization has relied on fixed-site measurements, leading to incomplete spatial and temporal coverage. In this study, we took advantage of satellite-derived PM2.5 data at a resolution of 0.01° × 0.01° and assessed the long-term PM2.5 trends on a city scale in the PRD region and on a district scale in Hong Kong from 2001 to 2015. The results of our analysis showed that the PM2.5 concentration in the PRD region and Hong Kong worsened during the 10th Five-Year-Plan (FYP) period (2001–2005) and started to improve during the 11th FYP period (2006–2010). The improvement accelerated during the 12th FYP period (2011–2015). These trends were in line with the trends of air-pollutant emissions. In addition, the cities in the central area of the PRD region experienced higher PM2.5 concentration increase during the 10th FYP period and a greater reduction in the concentration afterward during the 11th FYP period than those in the non-central area of the PRD region. During the 12th FYP period, PM2.5 concentrations substantially lessened for most of the cities in the PRD region, particularly for those in the non-central area of the PRD region. For Hong Kong, the rates of increase and decrease in the PM2.5 concentration generally exhibited gradients from the northwest to the southeast, suggesting effects from the regional transport of air pollutants. A group of districts in the central urban area of Hong Kong also experienced a substantial decline in the PM2.5 concentration during the 11th and 12th FYP periods, suggesting the beneficial effects of local control efforts.

In high-density cities, optimization of their compact urban forms is important for the enhancement of pollution dispersion, improvement of the air quality, and healthy urban living. This study aims to identify critical building morphological design factors and provide a scientific basis for urban planning optimization. Through a long-term mobile monitoring campaign, a four-month (spanning across summer and winter seasons) spatiotemporal street-level PM2.5 dataset was acquired. On top of that, the small-scale spatial variability of PM2.5 in the high-density downtown area of Hong Kong was mapped. Seventeen building morphological factors were also calculated for the monitoring area using geographical information system (GIS). Multivariate statistical analysis was then conducted to correlate the PM2.5 data and morphological data. The results indicate that the building morphology of the high-density environment of Hong Kong explains up to 37% of the spatial variability in the mobile monitored PM2.5. The building morphological factors with the highest correlation to PM2.5 concentration are building volume density, building coverage ratio, podium layer frontal area index and building height variability. The quantitative correlation between PM2.5 and morphological factors can be adopted to develop scientifically robust and straightforward optimization strategies for planners. This will allow considerations of pollution dispersion to be incorporated in planning practices at an early stage.

Evaluation of applied emission control programs for ambient air quality improvement in the short term is important for timely policy adjustment and air quality protection. However, this is generally a non-trivial task because of the possible contribution of various factors, such as the regional background effect, meteorological impact, and other source impacts, which must be quantified and eliminated from the observed concentration changes. In this study, we developed an ensemble assessment approach employing multiple methods: spatiotemporal analysis, wind pollution decomposition (WPD) analysis, elemental carbon (EC)-WPD hybrid analysis, and air quality modelling. By combining these methods, we showed that the contributions of different factors could be resolved, and the combined results of all methods formed a consistent result clearly demonstrating that a significant reduction in NOx (112.4 ± 25.9 μg/m3 reduction), NO2 (31.5 ± 6.0 μg/m3 reduction), and PM10 and PM2.5 (reduced by 6.8 μg/m3 and 4.5 μg/m3, respectively) occurred due to the impact of the applied control programs. This study could serve as a springboard for future vehicular emission control strategies in Hong Kong in the near future. Moreover, this holistic analysis could also provide an independent method for estimating the emission control impact in other megacities.

The ROx (=OH + HO2 + RO2) budget and O3 production at an urban site in central China (Wuhan) during autumn were simulated and analyzed for the first time using a UW Chemical Model 0-D box model constrained by in situ observational data. The daytime average OH, HO2, and RO2 concentrations were 2.2 × 106, 1.0 × 108, and 5.2 × 107 molecules cm−3, respectively. The average daytime O3 production rate was 8.8 ppbv h−1, and alkenes were the most important VOC species for O3 formation (contributing 45%) at this site. Our sensitivity test indicated that the atmospheric environment in Wuhan during autumn belongs to the VOC-limited regime. The daily average HONO concentration at this site during the study period reached 1.1 ppbv and played an important role in the oxidative capacity of the atmosphere. Without the source of excess HONO, the average daytime OH, HO2, RO2, and O3 production rates decreased by 36%, 26%, 27%, and 31% respectively. A correlation between the HONO to NO2 heterogeneous conversion efficiency and PM2.5 × SWR was found at this site; based on this relationship, if the PM2.5 concentration met the World Health Organization air quality standard (25 µg m−3), the O3 production rate in this city would decrease by 19% during late autumn. The burning of agricultural biomass severely affected the air quality in Wuhan during summer and autumn. Agricultural burning was found to account for 18% of the O3 formation during the study period. Our results suggest that VOC control and a ban on agricultural biomass burning should be considered as high-priority measures for improving the air quality in this region.

21 pags.,11 figs, 1 tab., Air pollution has been a hazard in China over recent decades threatening the health of half a billion people. Much effort has been devoted to mitigating air pollution in China leading to a significant reduction in primary pollutants emissions from 2013 to 2017, while a continuously worsening trend of surface ozone (O3, a secondary pollutant and greenhouse gas) was observed over the same period. Atmospheric oxidation, dominated by daytime reactions involving hydroxyl radicals (OH), is the critical process to convert freshly‐emitted compounds into secondary pollutants, and is underestimated in current models of China's air pollution. Halogens (chlorine, bromine, and iodine) are known to profoundly influence oxidation chemistry in the marine environment; however, their impact on atmospheric oxidation and air pollution in China is unknown. In the present study, we report for the first time that halogens substantially enhance the total atmospheric oxidation capacity in polluted areas of China, typically 10% to 20% (up to 87% in winter) and mainly by significantly increasing OH level. The enhanced oxidation along the coast is driven by oceanic emissions, and that over the inland areas by anthropogenic emission. The extent and seasonality of halogen impact are largely explained by the dynamics of Asian monsoon, location and intensity of halogen emissions, and O3 formation regime. The omission of halogen emissions and chemistry may lead to significant errors in historical re‐assessments and future projections of the evolution of atmospheric oxidation in polluted regions., This study received funding from the European Research Council Executive Agency under the European Union's Horizon 2020 Research and Innovation Programme (Project ERC‐2016‐ COG 726349 CLIMAHAL), and was supported by the Consejo Superior de Investigaciones Científicas (CSIC) of Spain, and The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and Shanghai Thousand Talents Program. The development and maintenance of the WRF‐Chem model are conducted by NOAA/ESRL/GSD in active collaboration with other institutes. Computing resources, support, and data storage were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory (CISL), sponsored by the NSF. The Fudan team acknowledges the financial support from National Natural Science Foundation of China (41775113). The HKPU team acknowledges financial support from the Hong Kong Research Grants Council (A‐PolyU502/16, T24‐504/17‐N). Data that support the finding of this study can be found at DOI: 10.17632/whz6yk79m4.1 (Li et al., 2020)