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Heat-induced labor loss is a major economic cost related to climate change. Here, we use hourly heat stress data modeled with a regional climate model to investigate the heat-induced labor loss in 231 Chinese cities. Results indicate that future urban heat stress is projected to cause an increase in labor losses exceeding 0.20% of the total account gross domestic product (GDP) per year by the 2050s relative to the 2010s. In this process, certain lower-paid sectors could be disproportionately impacted. The implementation of various urban adaptation strategies could offset 10% of the additional economic loss per year and help reduce the inequality-related impact on lower-paid sectors. So future urban warming can not only damage cities as a whole but can also contribute to income inequality. The implication of adaptation strategies should be considered in regard to not only cooling requirements but also environmental justice.

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.

Clean air policies have achieved remarkable air quality improvement in China for the last decade. However, as more importance was attached to climate issues and further improvement of air quality, policies with greenhouse gas (GHG) reduction potential were supposed to play a significant role. Here, we designed a conventional legislation pathway scenario (CLP) and an enhanced greenhouse gas reduction scenario (EGR), to estimate the co-effects of policies effective in GHG reduction on air pollutant control and air quality improvement in the Yangtze River Delta (YRD) region from 2014 to 2020, adopting a measure-specific evaluation method and an integrated WRF-CAMx model simulation. Results showed that: 1) With the implementation of enhanced measures with GHG reduction potential, emissions of SO

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 (

Hyperspectral remote sensing can capture the complicated and variable characteristics of inland waters; thus, it is suited for the water quality assessment of Case-2 waters, and it has the potentia...

The plume rising height of a ship will directly affect the maximum ground concentration and distance from the source caused by flue gas emission. Ship movement has an important effect on plume rising, but it is often ignored in previous studies. We simulated the weakening effect caused by ship movement by considering the influence of four main parameters (wind speed, ship speed, flue gas exit velocity, and flue gas exit temperature) on the smoke plume rising height, using the computational fluid dynamics (CFD) model (PHOENICS version 6.0 CHAM, London, UK). The main parameters affecting the difference in plume rising height between stationary and moving sources for the same parameter settings are the wind speed and the ship speed. Therefore, we established two simplified calculation methods that corrected the flue gas exit velocity (Vexit′) and the flue gas exit temperature (T′) for approximately simulating the smoke plume rising height of the moving ship using the formula of a stationary ship. Verification cases indicated that the corrected Vexit′(the average of relative error is 5.48%) and the corrected T′(the average of relative error is 60.07%) not only saved calculation time but also improved the simulation accuracy compared with the uncorrected stationary source scheme (the average of relative error is 135.38%). Of these correction methods, the scheme with corrected Vexit′ is more effective. The intention is to provide some references for the field experimentation of moving ship plume rising in different ports in the future and to further study the mechanism of moving ship plume rising.

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.

This study, for the first time, estimates the climate impacts of adopting green roofs and cool roofs on the seasonal urban climate of 16 cities that comprise the Yangtze River Delta metropolitan. We use a suite of regional climate simulation to compare the local climate impacts of the implementation of different roof strategies in summer and winter. The results indicate that in summer, the 2 m surface temperature reduced significantly when these two roof strategies are adopted, with peak reductions of 0.74 and 1.19 K for green roofs and cool roofs, respectively. The cooling impact of cool roofs is more effective than that of green roofs under the scenarios assumed in this study. Besides, rooted in the different mechanisms influencing urban heat flux, significant indirect effects were also observed: adopting cool roofs leads to a decreased precipitation in summer and an apparent reduction in wintertime temperatures in the urban area. Although cool roofs can be an effective way to reduce high temperatures during the summer, green roofs have fewer adverse impacts on other climate conditions. These results underline the need for comprehensive climate change policies that incorporate place-based solutions and extend beyond the nearly exclusive focus on summertime cooling.

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.

Urban trees’ cooling effect would change over time with different extents of warming. We conducted a comparative study of the greater Boston region (BOS) and Baltimore-Washington (BW) metropolitan area, two agglomerations with similar geographic and climatic conditions, as the study area. Cooling rate of urban trees (the changes in land surface temperature (LST) per fraction of tree cover) has been estimated based on the correlation between tree cover and LST on all extreme heat days over five years (2013–2017). We found that (1) the cooling rate of urban trees exhibited a nonlinear correlation with average LST across the two selected metropolitan areas, and the slope of the cooling rate increased rapidly when the average LST increased; (2) the average cooling rate was higher (0.12 °C/%) and more stable in BOS, and lower (0.10 °C/%) while fluctuated in BW; and (3) spatially, for promoting cooling effect, urban trees need to be more dispersed in Boston while more assembled in BW. More importantly, this difference tends to be more pronounced when the background LST increased. We think this different is caused by almost doubled wind speed in Boston than BW, even though they located in the same climate zone. This study further confirmed that not only the total cooling rate of urban trees, but the effects of spatial configuration will also be changed with different extents of outdoor warming in the same city.

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.

This work studied the impacts of ship emissions at a high temporal resolution on the real-time concentrations of PM2.5, NO2, and SO2 in urban harbors and coastal sea areas, taking the Yangtze River Delta (YRD) as an example. The WRF-Chem model with 3 nested grids and ship emissions derived from an automatic identification system (AIS) were combined to simulate the air quality. The AIS data showed significant temporal fluctuations in ship emissions, with hourly mean fluxes of approximately 1082.41 ± 444.41 and 593.55 ± 404.95 g/h/km2 near ports and in the channel waters of the YRD, respectively. The monthly mean contributions of shipping emissions reached 80.72% (2.15 ppbv) and 81.79% (8.79 ppbv) to ambient SO2 and NO2 in Ningbo Port, and 10.61% (6.96 μg/m3) to PM2.5 in Shanghai Port, respectively, regions with dense ship traffic. The relative differences in the PM2.5, SO2, and NO2 concentrations modeled using monthly and hourly ship emissions accounted for -10-15%, -10-30%, and - 5-30%, respectively. Compared with cruise- and land-based measurements, the simulations using hourly emissions were in much better agreement with the observations than those using monthly emissions and appropriately captured some air pollutant concentration peaks. Simulations during shipping-related periods with hourly ship emissions improved the normalized mean bias (NMBs) from -43.03%, 301.49%, and 223.02% to -27.28%, 90.45%, and 167.52%, respectively, for PM2.5, SO2, and NO2, highlighting the importance of using ship emissions with a fine temporal resolution. Our study showed that ignoring hourly fluctuations in ship emissions during air quality modeling leads to considerable uncertainties, especially in coastal urban areas and harbors with high ship activities. These results imply that data with a high temporal resolution, such as hourly ship emissions, are necessary to understand the realistic impacts of shipping traffic and to implement more precise control policies to improve coastal air quality.

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.

PM

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.

The acceleration of the industrialization process in China has increased the demand for electricity and triggered a power-plant building boom, especially along China’s eastern coast, where the economy gets off early and enjoys a fast development. The thermal plumes, residual chlorine, nuclides and other pollutions produced by the thermal and nuclear power plants have exerted an impact on the coastal eco-environment. To monitor the thermal pollution from the power plants at Yueqing Bay on the eastern coast, in this research, the distribution of sea surface temperature (SST) surrounding the power plants is obtained by using the SST retrieval methods developed for Landsat Enhanced Thematic Mapper Plus (ETM+), HJ-1B infrared sensor (IRS) and Terra moderate resolution imaging spectroradiometer (MODIS) data. The comparison of the SST retrieval results before and after the operation of power plants indicates that the total area of sea waters that is impacted by the thermal discharge from the two power plants at Yueqing Bay is approximately 17.95 km2, with the highest SST rise of 4.5 °C appearing over the waters around the outlet of the Huaneng Yuhuan power plant on the eastern shore, whereas the highest SST rise around the Zheneng Yueqing power plant on the western shore reaches 3.8 °C. The intensity and scope of influence of the thermal discharge mainly depend on the installed capacity of power plants, coastal terrain, and tide. Although the area where the SST rise is more than 3 °C is not large, thermal discharge still has an impact on bay ecosystems due to the relatively closed nature of the bay environment. Due to the influence of rising water temperatures on the reproduction and individual evolution of fish, shrimp, crabs, shellfish and other aquatic creatures, in the long term, the thermal pollution from coastal power plants will affect the volume of natural fishery and biological resources throughout the waters. The quantitative retrieval results also suggest that relative to MODIS data, Landsat ETM+ and HJ-1B IRS data with a high spatial resolution are more applicable to the estimation of small-scale SST, and IRS data with a high temporal resolution are more helpful in the study of spatio-temporal variability of thermal plumes from coastal power plants.

This study utilizes 521,631 activity data points from the 2007 Shanghai Pollution Source Census to compile a stationary carbon emission inventory for Shanghai. The inventory generated from our dataset shows that a large portion of Shanghai’s total energy use consists of coal-oriented energy consumption. The electricity and heat production industries, iron and steel mills, and the petroleum refining industry are the main carbon emitters. In addition, most of these industries are located in Baoshan District, which is Shanghai’s largest contributor of carbon emissions. Policy makers can use the enterpriselevel carbon emission inventory and the method designed in this study to construct sound carbon emission reduction policies. The carbon trading scheme to be established in Shanghai based on the developed carbon inventory is also introduced in this paper with the aim of promoting the monitoring, reporting and verification of carbon trading. Moreover, we believe that it might be useful to consider the participation of industries, such as those for food processing, beverage, and tobacco, in Shanghai’s carbon trading scheme. Based on the results contained herein, we recommend establishing a comprehensive carbon emission inventory by inputting data from the pollution source census used in this study.

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.

PM2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM2.5 from 2015 to 2018 in the YRDUA. Results showed that PM2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM2.5 and CO was the most consistent, and the correlation between PM2.5 and SO2/NO2 improved with the increase of time scale, while the correlation between PM2.5 and O3 was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

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.

This paper aims at combining principle component analysis (PCA) and artificial neural network (ANN) algorithm to predict chlorophyll a concentration in Dianshan Lake, Shanghai, eastern China. Firstly, based on field hyperspectral measurements, the sensitive wavelengths were selected as the input variables to build the basic ANN model, and the estimate accuracy (R 2) reached 0.85. In order to improve the accuracy and stability of the ANN model, the total nitrogen, total phosphorus, chemical oxygen demand, dissolve oxygen, and dissolved potential of hydrogen were selected as additional input variables. Consequently, the model accuracy increased to 0.9091. Further, aiming at eliminating the effect of inter-correlation of input variables, the PCA method was utilized to reduce the dimension of input variables. The result shows that the combined PCA–ANN model can reach an estimated accuracy with R 2 = 0.9184 and RMSE

The capability of obtaining spatially distributed air temperature data from remote sensing measurements is an improvement for many environmental applications focused on urban heat island, carbon emissions, climate change, etc. This paper is based on the MODIS/Terra and Aqua data utilized to study the effect of the urban atmospheric heat island in Shanghai, China. The correlation between retrieved MODIS land surface temperature (LST) and air temperature measured at local weather stations was initially studied at different temporal and spatial scales. Secondly, the air temperature data with spatial resolutions of 250 m and 1 km were estimated from MODIS LST data and in-situ measured air temperature. The results showed that there is a slightly higher correlation between air temperature and MODIS LST at a 250m resolution in spring and autumn on an annual scale than observed at a 1 km resolution. Although the distribution pattern of the air temperature thermal field varies in different seasons, the urban heat island (UHI) in Shanghai is characterized by a distribution pattern of multiple centers, with the central urban area as the primary center and the built-up regions in each district as the subcenters. This study demonstrates the potential not only for estimating the distribution of the air temperature thermal field from MODIS LST with 250 m resolution in spring and autumn in Shanghai, but also for providing scientific and effective methods for monitoring and studying UHI effect in a Chinese mega-city such as Shanghai.

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.

A study on commuter's exposure to PM 1 in Shanghai was presented. Commuters' real-time exposure concentrations by the common travel modes (bus, walking, cycling, taxi and subway) were measured by DustTrak™ dust monitors, and the average exposure concentrations and inhalation doses were analyzed. The lengths of the routes were designed to be within 30-min walking distance. The average PM 1 exposure concentrations during commuting were 0.147 ± 0.092 mg m −3 , 0.145 ± 0.088 mg m −3 , 0.140 ± 0.086 mg m −3 , 0.139 ± 0.086 mg m −3 and 0.122 ± 0.077 mg m −3 for the bus, walking, cycling, taxi and subway trips respectively. The concentrations in the microenvironments were 0.147 ± 0.096 mg m −3 , 0.155 ± 0.098 mg m −3 , 0.142 ± 0.092 mg m −3 and 0.079 ± 0.051 mg m −3 for the bus station, bus, taxi and train respectively. The concentrations in the microenvironments were close to those in Guangzhou and higher compared to the results in London, Sydney, Florence, etc. The combined effect of exposure concentration, travel time and inhalation rate led to different inter-mode relationship of inhalation doses compared to that of exposure concentrations. The inhalation doses were 54.5 ± 38.9 μg, 45.2 ± 30.1 μg, 29.2 ± 25.4 μg, 28.6 ± 25.9 μg and 12.8 ± 13.4 μg for the cycling, walking, bus, subway and taxi trips respectively. The outdoor fractions of the inhalation doses by bus, taxi and subway were much higher than the indoor fractions.

Two sensitivity simulations were performed and compared by model in order to understand how high-rise buildings influence meteorology and air quality in the Lujiazui Central Business District (CBD) of Shanghai, China. The coupled meteorological-photochemical model, Metphomod, was used, with a 500-m horizontal resolution and the observations and the simulated results generally agreed well. The scheme considering buildings within roughness could reduce uncertainties in the simulated meteorological conditions and concentrations of air pollutants. The high-rise buildings decreased wind speeds by 0.5–4 m/s, increased temperatures by up to 1 °C and turbulent kinetic energy by 1–2 J/m3 in the Lujiazui CBD. The changes in meteorological conditions also increased NO by about 2–5 %. However, the complex meteorological changes of higher temperatures and stronger turbulent kinetic energy, coupled with changes of precursors’ concentrations in the Lujiazui CBD, decreased O3 concentrations by up to 6 % somewhere, while increasing O3 formation by up to 2 % in downwind areas. The results suggested that it was necessary to include high-rise building parameters in models when estimating the meteorology and diagnosing air pollution of highly urbanized regions.

Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.

This study developed a prototype of an environmental assessment tool, SEA_LUP_SD, based on the system dynamics model. SEA_LUD_SD can simulate and evaluate information feedback of land use plan options and three related systems, population change, economic development, and environment conditions. This tool was then applied to the Shanghai land use master plan (1997–2010) and three other hypothetical planning options to compare the corresponding environmental impacts of different plan scenarios. This study proved that the integration of system dynamics and the framework of strategic environment assessment (SEA) has the great potential to provide urban managers and planners an effective technical platform to compare a series of “what if” scenarios for the entire planning horizon and to formulate policy recommendation on the basis of the simulation results. In addition, SEA_LUD_SD can help promote public participation in environmental decision-making processes and to educate decision- and policy-make...

China is now facing great challenges resulting from climate change and air pollution, driven by the processes of industrialization and urbanization. Greenhouse gas and air pollutant emissions produced by the coal-fired power industry represent approximately 70% of the total emissions in China’s industrial sector. In this study, 39 coal-fired power plants built in China between 2014 and 2015 were analyzed in regards to the co-effects oncarbon dioxide and air pollutant emissions generated directly and indirectly by end-of-pipe measures of pollution control. After completing the quantitative analysis with input data from 83units of power plants, we found that co-effects were positive only for air pollutant reductions through the implementation of desulfurization, denitrification, and dedusting measures, but co-effects were negative for carbon dioxide production because of the corresponding electricity use and chemical reactions that led to the increases in carbon dioxide emissions. We also performed an assessment of the synergistic coefficients to better understand the degree of co-effects. It will be important for researchers to take a comprehensive view of China’s coal-fired power plants and look for solutions that can maximize positive co-effects and achieve overall co-benefits of reductions in greenhouse gas emissions and air pollutants.

fluxes are 0.05–0.5 and 0.2–0.6 g m −2 yr −1 , respectively, with the wet deposition accounting for 79% of the total. The total deposition of inorganic nitrogen species, including NO3 ,N H4 , HNO3 ,N Ox, and NH3, to the eastern China seas is 498 GgN yr −1 and accounts for 3.4% of the nitrogen emission by China. Deposition of NO3 and NH4 dominates. The model results agree well with available in situ measurements. The deposition of NH4 and NH3 to the East China Sea is up to 166 GgN yr −1 , which nearly equals the total input of 184 GgN yr −1 from the mainland, including riverine discharge, industrial wastewater, and domestic wastewater. Deposition of atmospheric ammonium can account for 56% of the external total input, which is 1.1–1.5 times the input from the major rivers to all the eastern China seas. Ammonium deposition to the Yellow Sea accounts for as much as 87% of the total input. The annual total nitrogen deposition can be converted to new primary biological productivity of 100–200 mmol C m −2 yr −1 ,o r 1.1–3.9% of the new productivity in the East China Sea. Our results suggest that atmospheric deposition has important impact on biological productivity in all the eastern China seas.

This paper presented the development of an expert system (ES) and its integration with GIS and mathematical models to advance a method to identify the significant atmospheric environmental impact caused by road traffic. Based on the literature reviews and consultations with experts, the paper addressed two major impact factors-photochemical smog and visibility for which ES knowledge base and reasoning had been developed. The development of GIS platform was based on the ArcObjects, and the ES was developed using ClipsActiveXControl. Finally, a case study of the screening of atmospheric environmental impact of comprehensive road traffic planning in Shanghai was presented. The case study shows that ES and its integration with GIS and mathematical models are supportive for the preliminary assessment of atmospheric environmental impact of urban traffic planning, and the component technology is an important means for system integration, which will improve the efficiency and flexibility of system development.