Search

In view of the spatial and temporal gaps left by the ground monitoring network for fine particulate matter (PM2.5), remote sensing has been regarded as an effective monitoring alternative to provide data of the PM2.5 distribution at a high resolution. Satellite-derived aerosol optical depth (AOD) is a parameter that has a good correlation with the PM2.5 concentration. In this study, a modified deep learning model was established to estimate hourly PM2.5 concentrations in the Yangtze River Delta Urban Agglomeration (YRDUA) region based on 5-km AOD data from Himawari-8. The model incorporated a density-based spatial clustering of applications-with-noise (DBSCAN) cluster analysis and a deep neural network (DNN) (denoted as DBSCAN–DNN) to construct individualized DNNs for the retrieval of PM2.5. The DBSCAN algorithm was used to identify the outlying datasets by involving the relationship between AOD–NO2 and PM2.5. The cluster analysis divided the inputs into separated clusters with distinct pollution levels, which helped to build a reference for the construction of individualized DNNs. The results showed that the DBSCAN–DNN model could greatly improve the estimation accuracy of the PM2.5 concentration based on identical inputs when compared with the pure DNN model. The 10-fold cross-validation R-value was enhanced by over 30%, with the highest R-value reaching 0.94 when applied to Shanghai dataset of 2018. The root-mean-square prediction error (RMSE) was also reduced by over 30%. In addition, the model performed well in generating hourly spatial estimations, thereby showing more detailed information for the dynamic changes of the PM2.5 concentration during the day. Moreover, according to the comparisons of five regional estimation results, the model was proven to have a good applicability to deal with a differentiated data volume and data complexity. This study not only proposes a new method to achieve PM2.5 estimations with a higher accuracy and spatiotemporal resolution, but also provides a new perspective for exploiting the sophisticated correlations among large environmental datasets by introducing pre-clustering into the deep learning approach.

A better understanding of river capacity for contaminants (i.e., water environmental capacity, WEC) is essential for the reasonable utilization of water resources, providing government’s with guidance about sewage discharge management, and allocating investments for pollutant reduction. This paper applied a new framework integrating a modified hydro-environmental model, Soil and Water Assessment Tool (SWAT) model, and load–duration curve (LDC) method for the dynamic estimation of the NH3-N WEC of the data-scarce Luanhe River basin in China. The impact mechanisms of hydrological and temperature conditions on WEC are discussed. We found that 77% of the WEC was concentrated in 40% hydrological guarantee flow rates. While the increasing flow velocity promoted the pollutant decay rate, it shortened its traveling time in streams, eventually reducing the river WEC. The results suggest that the integrated framework combined the merits of the traditional LDC method and the mechanism model. Thus, the integrated framework dynamically presents the WEC’s spatiotemporal distribution under different hydrological regimes with fewer data. It can also be applied in multi-segment rivers to help managers identify hot spots for fragile water environmental regions and periods at the basin scale.

Dimethylsulfide (DMS) emitted from seawater is a key precursor to new particle formation and acts as a regulator in Earth's warming climate system. However, DMS's effects are not well understood in various ocean regions. In this study, we estimated DMS emissions based on a machine learning method and used the GEOS-Chem global 3D chemical transport model coupled with the TwO Moment Aerosol Sectional (TOMAS) microphysics scheme to simulate the atmospheric chemistry and radiative effects of DMS. The contributions of DMS to atmospheric SO42- aerosol and cloud condensation nuclei (CCN) concentrations along with the radiative effects over the Asian region were evaluated for the first time. First, we constructed novel monthly resolved DMS emissions (0.5∘×0.5∘) for the year 2017 using a machine learning model; 4351 seawater DMS measurements (including the recent measurements made over the Chinese seas) and 12 relevant environment parameters were selected for model training. We found that the model could predict the observed DMS concentrations with a correlation coefficient of 0.75 and fill the values in regions lacking observations. Across the Asian seas, the highest seasonal mean DMS concentration occurred in March–April–May (MAM), and we estimate the annual DMS emission flux of 1.25 Tg (S), which is equivalent to 15.4 % of anthropogenic sulfur emissions over the entire simulation domain (which covered most of Asia) in 2017. The model estimates of DMS and methane sulfonic acid (MSA), using updated DMS emissions, were evaluated by comparing them with cruise survey experiments and long-term online measurement site data. The improvement in model performance can be observed compared with simulation results derived from the global-database DMS emissions. The relative contributions of DMS to SO42- and CCN were higher in remote oceanic areas, contributing 88 % and 42 % of all sources, respectively. Correspondingly, the sulfate direct radiative forcing (DRF) and indirect radiative forcing (IRF) contributed by DMS ranged from −200 to −20 mW m−2 and from −900 to −100 mW m−2, respectively, with levels varying by season. The strong negative IRF is mainly over remote ocean regions (−900 to −600 mW m−2). Generally, the magnitude of IRF derived by DMS was twice as large as its DRF. This work provides insights into the source strength of DMS and the impact of DMS on climate and addresses knowledge gaps related to factors controlling aerosols in the marine boundary layer and their climate impacts.

Due to increased concerns regarding air pollutants emitted from shipping, feasible technology for the surveillance of these pollutants is in high demand. Here, we present shore-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of SO2 and NO2 emitted from ships under different traffic conditions in China's ship emission control areas (ECAs) in Shanghai and Shenzhen, China. Three typical measurement sites were selected in these two regions to represent the following emission scenarios: ships docked at berth, ships navigating in an inland waterway and inbound/outbound ships in a deep-water port. Using 2-D scanning, the observations show that SO2 and NO2 hot spots can be quickly and easily located from multiple berths. Although MAX-DOAS measurements can not distinguish plumes from specific ships in the busy shipping lanes of the inland waterway area, they certify that variations in the SO2 and NO2 levels are mainly impacted by the ship traffic density and the atmospheric dispersion conditions. In the open water area, which has a lower vessel density, MAX-DOAS measurements can capture the pulse signal of ship-emitted SO2 and NO2 very well; they can also characterize the peak's altitude and the insistent duration of the individual ship plumes. Combined with the ship activity data, information on the rated power of the engine and the fuel sulfur content, it was found that the SO2∕NO2 ratio in a single plume is usually low (

Rapid urbanisation has altered the vulnerability of urban areas to heat wave disasters. There is an urgent need to identify the factors underlying the effect of heat waves on human health and the areas that are most vulnerable to heat waves. In this study, we plan to integrate indices associated with heat wave vulnerability based on meteorological observation data, remote sensing data and point of interest (POI) data; analyse the influence of urbanisation on the urban vulnerability environment; and explore the relationship between the vulnerability environment and heat-wave-related mortality. Finally, we attempt to map the spatial distribution of high heat-wave-related mortality risk based on the results of heat wave vulnerability study and artificial society. The results reveal that 1) there are differences in the influence of urbanisation on heat wave exposure, sensitivity and adaptability; 2) the exposure and sensitivity level effects on the lower limit of health impacts and the adaptability level effects on the upper limit of the health impact from heat wave in a given study area; and 3) areas vulnerable to the effects of heat waves are not confined to the city centre, which implies that residents living in suburban areas are also vulnerable to heat waves. Finally, this study not only explores the factors contributing to the impacts of heat waves but also describes the spatial distribution of the risk of disaster-associated mortality, thereby providing direct scientific guidance that can be used by cities to address heat wave disasters in the future.

In spite of the state-of-the-art performances of machine learning in the PM

The implementation of pollutant emission control has made initial achievements in the plant power, iron, and steel industries in China. To further improve air quality, it is of great significance to carry out research on zero-impact emissions of the petrochemical industry. Based on the existing concept and practice of zero emissions, this study proposes the concept of zero-impact emissions, taking emission concentration as the constraint. A typical petrochemical enterprise (namely Enterprise A) in Shanghai Chemical Industry Park as the research object, and used the CALPUFF model to simulate the target pollutant emissions (i.e. sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter (PM), and volatile organic compounds (VOCs)). The current emission standard, spatial distributions, and emission heights of chimneys in Enterprise A was considered as the baseline emission scenario and taking the zero-impact emission as a target for simulation. The results show that the current emission standards of NOx and VOCs (benzene) exceeded the zero-impact emission limits, and needed to be reduced by 22% and 87.5%, respectively. Moreover, the areas that exceeded the zero-impact concentration limits were located northwest of the chimneys and Hangzhou Bay. In terms of seasonal effects, the wind conditions in spring were more adverse for the enterprise to achieve zero-impact emissions. Based on the simulation, the zero-impact emission limits of SO2, NOx, PM, and VOCs (benzene) for Enterprise A were 50 mg/m3, 78 mg/m3, 10 mg/m3, and 0.32 mg/m3, respectively. Implications: Through case study, this paper solves the environmental management issue which is of universal significance for chemical industry park. The concept of zero-impact emissions and the determination method of zero-impact concentration limits proposed in this study could be used as references for related research on cutting emissions. Although the conclusion of this study about the emission limits is not suitable for other enterprises to apply directly, the calculation method of zero-impact emission limit can be used by other enterprises. Furthermore, the zero-impact emission limits on park scale can be determined after comprehensive evaluation based on the calculation results of multiple enterprises.

Reducing industrial emissions has become increasingly important, given China&rsquo, s ongoing industrialization. In this study, the reduction in CO2 emissions and air pollutants due to end-of-pipe treatment in a typical integrated steel plant in China was assessed. The emissions were subdivided into sector levels, including main production and auxiliary departments. The synergies of reducing air pollutants and CO2 emissions using end-of-pipe treatment technologies were quantified, including direct and indirect effects. The results show that (1) using the carbon balance method is more suitable for the greenhouse gas (GHG) emissions of the steel plants in China at the enterprise and sector levels. The carbon-related parameters adopted in the carbon balance method strongly impact the accuracy of the emission calculation. (2) Compared with the direct synergistic CO2 emissions caused by chemical reactions, the indirect emissions due to the power consumption of the end-of-pipe facilities is more significant. (3) To achieve the control of local air pollutants and CO2 emissions, the negative effects of CO2 emissions caused by the end-of-pipe treatment technologies should be considered.

In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter10 μm [PMThe authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality. Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.

Enterprises, which are the entities that must implement emission reduction policies, as well as other regional management bodies exhibit significant differences with respect to certain indicators. Previous studies mainly focus on one-indicator or one-sector evaluation methods, which are overly simple and inadequate for enterprise analysis. To solve this problem, this paper selects three key indicators for enterprise evaluation and proposes an integrated classification approach grouping enterprises into sets. The proposed approach comprises one main model and three sub-models. The sub-models (Emission Model, Data Envelopment Analysis Model and Geo-spatial Model) pre-compute the indexation of emissions, overall efficiency and geo-location conditions as input data for the main model. The main model synthesizes these three sub-models’ inputs and classifies enterprises into different classification sets. For verification, we conduct a case study of 8106 manufacturing enterprises in Shanghai. In the case study, the proportions of enterprises in top set, middle-top set, middle-low set and low set were 19.5%, 47.5%, 29.4% and 3.6%, respectively. The classification approach provides adequate quantitative technical support for flexible policy design and has the potential to promote efficient regional emission reduction, optimize regional industrial structures and distribute enterprises rationally.

Urban activities are the primary source of carbon emissions. With the accelerating development of urbanization, cities are now facing the dual pressures of maintaining economic growth and reducing carbon emissions, and mega-cities like Shanghai play a crucial role in emission reduction. An integrated system dynamic model (including eight sub-models, which are socio-economic; primary, secondary, and tertiary industry; residential; transportation; waste disposal; and electricity models) is developed using the Vensim platform for evaluating carbon emission trends in Shanghai during 1991–2015, from the perspective of an urban planning system. The results show a general increasing trend in total carbon emissions that reached 245.78 million tons CO2-equivalent (Mt CO2-eq) in 2015, which is nearly three times as much as that in 1991. This study also shows that the electric power sector is the main contributor to carbon emissions. Five emission-reduction scenarios were generated by inputting values of planning indicators from the National Economy and Social Development Plan (NESDP) that have direct and indirect impacts on carbon emissions. According to the results of this analysis, the lowest level of carbon emissions is from a scenario with slower socio-economic development and reinforced electrical and industrial energy efficiency programs (Scenario IV), which demonstrates that the appropriate control of energy consumption from secondary industry (especially the electricity sector) will play a positive role in carbon emission mitigation in Shanghai. Outcomes of this study can provide essential information for policy-makers to advance Shanghai's future low-carbon development. These outcomes could also guide similar studies modeling CO2 emissions from the perspective of urban planning systems.

With the development of agriculture, industry and urbanization, land-use and land-cover (LULC) change has resulted in significant deterioration of water quality and severe eutrophication in most of the lakes in China. Plateau lake ecosystem in China is very vulnerable and especially sensitive to environmental change and human disturbance, due to its strong closeness, species simplification, oligotrophy and simple food chain. This research focuses on evaluating the quantitative and spatial relationships of land use pattern and water quality of rivers inflowing to Fuxian Lake, China’s largest deep freshwater lake in plateau. To investigate the influence of spatial variation in land-use structures and topography on rivers water quality, the distributions of land-use types in the lake’s drainage basin were obtained from satellite images, and the correlations between land-use types and inflow water quality indicators were examined by applying statistical analysis and spatial analytical function of Geographic Information System. Subarea-level analysis reveals that a land-use type could exert different effects on water quality in plains and mountains, and the effects had a connection with topographic and hydrologic factors, its mixture with other land-use types, weather conditions during field measurements, as well as its scale. In addition, a comparison of correlation coefficient data for buffer regions of different scales indicated that the effect of land-use type on inflow water quality peaked in buffer regions with a radius between 100 and 200 m. On the whole, the regions within 200 m of river banks were the key regions that affected river water quality, and thus the construction and preservation of a riparian buffer zone in these regions can provide considerable protection from the inputs of non-point source pollutants and nutrients, and important function such as water and soil conservation. Based on these, a pollution control zoning was constructed from two key pollution control zones in the north and south, a phosphate rock pollution control zone in the northeast, a water loss and soil erosion control zone in the east, and a tourism pollution control zone in the west. This research also offers valuable insights into how to carry out subarea-level prevention and control of water pollution and regional development, according to natural environment, land use pattern and characteristics of pollution sources in different pollution control zones.

China has been in the implementation phase of Domestic Ship Emission Control Areas (DECAs) regulation to reduce emissions of air pollutants from ships near populated areas since 2016. The Yangtze River Delta (YRD) is one of the busiest port clusters in the world, accounting for 11% of global seaborne cargo throughput, so future improvements in shipping emission controls may still be important in this region. To assess the impact of future ship emissions on air quality of coastal areas, this study evaluates emissions reductions and air quality in 2030 for three scenarios (business as usual, stricter regulations, and aspirational policies) representing increasing levels of control compared with a base year of 2015. We projected ship emissions in the region using a bottom-up approach developed in this study and based on the historical ship automatic identification system (AIS) activity data. We then predicted air quality across the YRD region in 2030 using the Community Multiscale Air Quality (CMAQ) model. The annual average contributions of ship emissions to ambient PM2.5 would decrease by 70.9%, 80.4%, and 86.2% relative to 2015 under the three scenarios, with the largest reductions of more than 4.1 μg/m3 near Shanghai Port under the aspirational scenario. Reductions in ship emissions generally led to lower levels of PM2.5, particularly in most of the coastal cities in the YRD. Compared with a business-as-usual approach the aspirational scenario reduced SO2, NOx and PM2.5 concentrations from shipping by 71.8%, 61.1% and 52.5%, respectively. It was also more effective than the stricter regulation scenario, suggesting that the requirement to use 0.1% sulfur fuel within a 100Nm DECA would have additional benefits to ambient PM2.5 concentrations beyond 12Nm DECA area. This study provides evidence to inform deliberations on the potential air quality benefits of future control policies for ship emissions in China.

In urban areas, concentrated populations and societal changes intensify the influence of climate change. However, few studies have focused on vulnerability to climate-related risks on the scale of a single urban area. Against this backdrop, we reconstructed a spatial vulnerability framework based on the drivers-pressures-state-impact-response (DPSIR) model to reflect the complex interactions between urbanization and climate change and to integrate the natural and socio-economic factors of urban areas into this framework. Furthermore, to explore the relationship between rapid urbanization and climate change, we studied data from two years that represented different stages of urbanization. The results showed that the index framework was able to reconcile these two concepts to reflect the complex interactions between urbanization and climate change. The assessment results indicate that the overall degree of climate change vulnerability exhibits a generally increasing and dispersing trend after rapid urbanization. The increasing trend is influenced by an increase in low-vulnerability areas, and the dispersing trend is influenced by anthropogenic activities caused by rapid urbanization. The changes are reflected in the following observations: 1. The suburbs are affected by their own natural environmental characteristics and rapid urbanization; the vulnerability level has risen in most areas but has declined in certain inland areas. 2. High-vulnerability regions show minor changes during this stage due to the lasting impact of climate change. Finally, the main environmental problems faced by high-vulnerability areas are discussed based on existing research.

Urbanization significantly influences meteorological conditions and air quality. Statistically, air pollution in the megacity of Shanghai usually occurs with cold weather fronts. An air pollution episode during a cold front was simulated using weather research and forecasting and the Community Multi-scale Air Quality model system. In this study, we used two urban schemes, a simple bulk scheme (denoted BULK) and the single-layer urban canopy model (SLUCM), to check the effects of urban parameterization. Our results showed that SLUCM better predicted the arrival time and cooling process of the cold front and more realistically simulated the moving process of the cold front. The improvement in the temperature and relative humidity simulation achieved using SLUCM was more effective under higher urbanization levels, whereas the wind speed simulation was better in rural areas. The simulated concentrations at sites with high urbanization were obviously improved by urban parameterization. The barrier role of the urban canopy during a cold front was better represented and was shown to cause a wider polluted area and higher pollutant concentration using SLUCM than with BULK. Overall, accurate meteorological simulations in the atmospheric boundary layer using SLUCM are expected to provide good prediction of urban air quality.

In estuary areas, meteorological conditions have become unstable under the continuous effects of climate change, and the ecological backgrounds of such areas have strongly been influenced by anthropic activities. Consequently, the water quality of these areas is obviously affected. In this research, we identified periods of fluctuation of the general meteorological conditions in the Yangtze River Estuary using a wavelet analysis. Additionally, we performed a spatiotemporal evaluation of the water quality in the fluctuating period by using remote sensing modeling. Then, we explored how the fluctuating meteorological factors affect the distribution of total suspended solids (TSS) and chlorophyll-a (Chla) concentration. (1) The results show that from 2000 to 2015, temperature did not present significant fluctuations, while wind speed (WS) and precipitation (PR) presented the same fluctuation period from January 2012 to December 2012. (2) Based on the measured water sample data associated with Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we developed a water quality algorithm and depicted the TSS and Chla concentrations within the WS and PR fluctuating period. (3) We found that the TSS concentration decreased with distance from the shore, while the Chla concentration showed an initially decreasing trend followed by an increasing trend, moreover, these two water quality parameters presented different inter-annual variations. Then, we discussed the correlation between the changes in the TSS and Chla concentrations and the WS and PR variables. The contribution of this research is reflected in two aspects: 1. variations in water quality parameters over a wide range of water bodies can be evaluated based on MODIS data, 2. data from different time periods showed that the fluctuations of meteorological elements had different impacts on water bodies based on the distance from the shore. The results provide new insights for the management of estuary water environments.

Based on the feasibility evaluation of measuring individual ship emissions, layout of onshore monitoring sites in Yantian Port, China was studied. Four categories of representative ships were selected and WRF/CALPUFF was used to simulate the transmission and diffusion of exhaust pollutants from each individual ship. Statistical analysis was conducted on the measurable SO2 range for all categories of representative ships under 24 types of typical meteorological conditions. The measurable extent was the largest when the wind direction was perpendicular to the route. Additionally, the measurable distance increased with increasing wind speed under stable stratification, while the opposite held true under unstable stratification. Moreover, the ship's main engine power had significant impact on the measurable extent. Overall, the most favourable monitoring direction is southwest and establishing monitoring sites on the southern island of Yantian Port was deemed optimal. The measurable distances of category A, B, C and D ships southwest of the monitoring sites were large, at 2.55 km, 2.64 km, 3.04 km, and 3.28 km, respectively. The measurable distance was 2.64 km on the annual average scale in this direction. The monitoring efficiencies of the six pre-set monitoring sites ranged from 15.32% to 64.34%; Sites 5 and 6, located on the southern island, had the highest monitoring efficiencies, followed by Site 3 north of the ship routes. The monitoring efficiency would reach 98.16% if onshore monitoring sites were set up simultaneously at Site 5 and Site 3.

To study the impact of vessel pollution on the atmospheric environment of the surrounding area, we present a numerical simulation method based on regional emissions inventories. The general spatial resolution is &ge, 1 km and the temporal resolution is &ge, 1 h, parameters which are suitable for the study of larger space&ndash, time scales. In this paper, the WRF/CALMET/CALPUFF model and Automatic Identification System (AIS) data are employed to develop a single-vessel atmospheric pollution diffusion model. The goal of this research uses existing meteorological models and diffusion models to provide a simulation technology method for studying the diffusion of SO2 from a single ship. We take the outgoing phase of ocean-going container vessels in Yantian Port as an example. It can be used to set the position of sensitive receptors near the port area. Simulations are implemented with CALPUFF and the results are compared with data derived from on-site monitoring instrument. The CALPUFF modelling domain covers an area of 925 km2 with a grid spacing of 500 m. The simulation results demonstrated agreement with the measured data. The ground concentration contribution value ranged from 10 to 102 &mu, g/m3, while the affected area was about 4&ndash, 26 km2 and the high-value area of the ground concentration contribution was distributed within 1&ndash, 2 km from the ship track. Emissions generated by the vessels represent a considerable contribution to SO2 pollution around the harbor areas.

Many cities are developing mitigation plans in an effort to reduce the population health impacts from expected future increases in the frequency and intensity of heat waves. To inform heat mitigation and adaptation planning, information is needed on the extent to which available mitigation strategies, such as reflective and green roofs, could result in significant reductions in heat exposure. Using the Weather Research and Forecasting (WRF) model, we analysed the impact of green and cool (reflective) roofs on the urban heat island (UHI) and temperature-related deaths in the Greater Boston area (GBA) and New England area (NEA) in summer and winter. In the GBA, green and cool roofs reduced summertime population-weighted temperature by 0.35 °C and 0.40 °C, respectively. In winter, green roofs did not affect temperature, whereas cool roofs caused a temperature reduction of 0.40 °C. In the NEA, the cooler summers induced by green and cool roofs were estimated to reduce the heat-related mortality rates by 0.21% and 0.17%, respectively, compared to baseline. Cool-roof-induced temperature reduction in winter could increase the cold-related mortality rate by 0.096% compared to baseline. These results suggest that both green and cool roofing strategies have the potential to reduce the impact of heat on premature deaths. Additionally, the differing effects in winter suggest the need for a careful consideration of health trade-offs in choosing heat island mitigation strategies.

The Yangtze River Delta (YRD) and the megacity of Shanghai are host to one of the busiest port clusters in the world, the region also suffers from high levels of air pollution. The goal of this study was to estimate the contributions of shipping to emissions, air quality, and population exposure and characterize their dependence on the geographic spatiality of ship lanes from the regional scale to city scale for 2015. The WRF-CMAQ model was used to simulate the influence of coastal and inland-water shipping, in port emissions, shipping-related cargo transport on air quality and, population-weighted concentrations, a measure of human exposure. Our results showed that the impact of shipping on air quality in the YRD was attributable primarily to shipping emissions within 12 NM of shore, but emissions coming from the coastal area of 24 to 96 NM still contributed substantially to ship-related PM2.5 concentrations in YRD. The overall contribution of ships to PM2.5 concentration in YRD could reach to 4.62 μg/m3 in summer when monsoon winds transport shipping emissions onshore. In Shanghai city, inland-water going ships were major contributors (40–80 %) to the shipping impact on urban air quality. Given the proximity of inland-water ships to urban populations of Shanghai, the emissions of inland-water ships contributed more to population-weighted concentrations. These research results provide scientific evidence to inform policies for controlling future shipping emissions; in particular, stricter standards could be considered for the ships on inland rivers and other waterways close to residential regions.

Amines have received increasing attention in recent years because of their potential role in new particle formation in the atmosphere and their impact on aerosol chemistry. High concentrations of amines are expected to be limited to the vicinity of source regions due to their short lifetime, highlighting the necessity of having a better understanding of contributions of emissions from different source types. This study presents the first high-resolution model simulation of methylamines concentrations on a regional scale over the Yangtze River Delta region in east China. The WRF-Chem with nested grids is used in model simulations. In contrast to the very limited existing modeling studies that assumed a fixed ratio (FR) of amines to total ammonia emission, we derive source-dependent ratios (SDR) that distinguish C1-amine (CH3NH2), C2-amines (C2H7N), C3-amines (C3H9N) emissions from five different source types (agriculture, residential, transportation, chemical industry, and other industry). The amines-to-ammonia mass emission ratios, estimated from previous measurements, are 0.026, 0.0015, 0.0011, 0.0011, and 0.0011 for C1-amine, 0.007, 0.0018, 0.0015, 0.01, and 0.0009 for C2-amines, and 0.0004, 0.0005, 0.00043, 0.0006, and 0.0004 for C3-amines for chemical-industrial, other industrial, agricultural, residential, and transportational sources, respectively. The simulated concentrations of C1-, C2-, and C3-amines, based on both FR and SDR, have been compared with field measurements at a suburban site in Nanjing and at an urban site in Shanghai, China. SDR substantially improves the model’s ability in capturing the observed concentrations of methylamines. C1-, C2-, and C3-amines concentrations in the surface layer in the Yangtze River Delta region are generally in the range of 2–20 pptv, 5–50 pptv, and 0.5–4 pptv. Vertically, the concentrations of C1-, C2-, and C3-amines decrease quickly with altitude, dropping by a factor of ~ 10 from the surface to ~ 900 hPa. Results from the present study are critical to evaluating potential roles of amines in nucleation and chemical processes in polluted air.

Air pollution episodes of unknown origins are often detected by online equipment for air quality monitoring in industrial parks in China. The number of monitors available to provide observation data, as well as the source information, is often very limited. In such case, the identification of a potential source area is more practical than the precise back-calculation of the real source. The potential source area which can be deduced from the observation data from limited monitors was concerned in this paper. In order to do the source area identification, two inverse methods, a direct method and a statistical sampling method, were applied with a Gaussian puff model as the forward modeling method. The characteristic of the potential source area was illustrated by case studies. Both synthetic and real cases were presented. The distribution of the source locations and its variation with the other unknown source parameters were mainly focused in the case study. As a screening method, source area identification can be applied not only when the number of effective monitors is limited but also when an ideal number of monitors are available as long as the source information is almost uncertain.

Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

This paper presents a carbon emission inventory in Shanghai for the period from 2000 to 2008 covering six sectors of stationary combustion, transportation, industrial processes, waste disposal and treatment, agricultural activities and forestry sink and three greenhouse gases (CO2, CH4, and N2O). The aim is to reflect carbon emissions in Shanghai, and guides policymakers to take effective actions to mitigate carbon emissions. Several results are obtained: (1) the total carbon emissions in Shanghai increased by 48%, from 136 Tg CO2e in 2000 to 200 Tg CO2e in 2008; (2) only the sector of agriculture activities saw reduced emissions; (3) the comparisons among Shanghai, China and world average level confirm that during 2000–2008 Shanghai's carbon emissions per capita are higher than both the world and the China average level, and its carbon emission per GDP is also higher than the world average level, but both of them are lower than the China average level; (4) in 2008, Shanghai's carbon emission per GDP is around 10 tons CO2 per $10,000 and higher than that of Taiwan, Hong Kong, the G7 and the BRIC (Brazil, Russia, India and China) except China and India. In addition, this paper also illustrates the problems about China statistical system in terms of emission inventory establishment, including the classification system, data quality and temporal resolution.

To characterize the air pollutants in Shanghai Port and identify the contribution from ship traffic emission, field measurements have been conducted in 2011. The trace gases SO 2 , NO 2 and O 3 were monitored and aerosol samples of TSP, PM 2.5 and size-segregated particles were collected in a working area of Shanghai Port. Elements including V, Ni, Al, Fe, Si, Ca, Na, Mg, Mn, Zn, Co, Cr in aerosol samples and heavy fuel oil samples were analyzed. The results revealed that average hourly SO 2 and NO 2 concentrations in Shanghai Port were respectively 29.4 and 63.7 μg m −3 , average daily concentrations of TSP and PM 2.5 were 114.39 and 62.60 μg m −3 , comparable with the ones in Shanghai land area. Ni and V were found enriched in fine particles with averaged concentrations of 80.0 and 14.8 ng m −3 in PM 2.5 respectively. Also ratio of V/Ni in aerosol under summertime airflow was 3.4, very close to the ratio of averaged V and Ni content in international heavy fuel oils used in Shanghai Port. The backward trajectory analysis further revealed that SO 2 , NO 2, and V under coastal airflows were mainly from ship traffic emission. The mean concentration of V was 15.84 ng m −3 under hybrid coastal airflows, much higher than that of 9.84 ng m −3 under continental airflows. Furthermore, V was found to be highly correlated with ship fluxes, and was selected as an indicator of ship traffic emission in Shanghai. The estimated primary PM 2.5 contribution from ship traffic ranged from 0.63 to 3.58 μg m −3 , with an average of 1.96 μg m −3 . This PM 2.5 fraction accounted for 4.23% of the total PM 2.5 in an average level, and reached to a maximum of 12.8%. Furthermore, there could be 64% of primary PM 2.5 contributed by ships in Shanghai Port transported to inland region. Our results suggest that ship traffic has a non-negligible contribution on ambient levels of fine particles and secondary contribution of SO 2 and NO 2 emitted by ships need to be estimated on local and regional scale in future.