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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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Field measurements of aerosols were conducted on Xiaoyangshan Island in the East China Sea in May 2006 to investigate the impact of anthropogenic air pollutants to the coastal environment. MM5/CMAQ was used to model the development of the episode and identify the sources of major ionic species during the episode. The results showed that the major ionic species in TSP were SO4(superscript 2-), NO3(superscript 2-) and NH4(subscript +), which accounted for about 76% of the total water soluble ions (TWSI). The mean concentrations of SO4(superscript 2-), NO3(superscript -) and NH4(superscript +) were 20.9, 10.4 and 5.1 μg m^(-3), respectively, and the peak values on a foggy day were 69.87, 38.48 and 22.75μg m^(-3), respectively. The most abundant ionic species was SO4(superscript 2-). The increase of SO4(superscript 2-) concentration was found to be significantly correlated with the decrease of the atmospheric visibility (correlation coefficient = 0.82). Process analysis implied that advection, diffusion and cloud chemical processes contributed to this pollution episode. Backward trajectory analysis revealed that the airflow on the foggy and most polluted day during the sampling campaign was westerly, i.e., from the continent. Both the measured data and modeling results showed that the coastal atmosphere was seriously affected by terrestrial pollutants in the continental outflow.