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Mobile observation has been widely used in the monitoring of air pollution. However, studies on pollution sources and emission characteristics based on mobile navigational observation are rarely reported in the literature. A method for quantitative source analysis for industrial air pollutant emissions based on mobile observations is introduced in this paper. NOx pollution identified in mobile observations is used as an example of the development of the method. A dispersion modeling scheme that fine-tuned the meteorological parameters according to the actual meteorological conditions was adopted to minimize the impact of uncertainties in meteorological conditions on the accuracy of small-scale dispersion modeling. The matching degree between simulated and observed concentrations was effectively improved through this optimization search. In response to the efficiency requirements of source resolution for multiple sources, a random search algorithm was first used to generate candidate solution samples, and then the solution samples were evaluated and optimized. Meanwhile, the new index Smatch was established to evaluate the quality of candidate samples, considering both numerical error and spatial distribution error of concentration, in order to address the non-uniqueness of the solution in the multi-source problem. Then, the necessity of considering the spatial distribution error of concentration is analyzed with the case study. The average values of NOx emission rates for the two study cases were calculated as 69.8 g/s and 70.8 g/s. The Smatch scores were 0.92–0.97 and 0.92–0.99. The results were close to the online monitoring data, and this kind of pollutant emission monitoring based on the mobile observation experiment was initially considered feasible. Additional analysis and clarifications were provided in the discussion section on the impact of uncertainties in meteorological conditions, the establishment of a priori emission inventories, and the interpretation of inverse calculation results.

Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in environmental research. Developing a robust air quality predictive model has become an increasingly important task, holding practical significance in the formulation of effective control policies. Recently, deep learning has progressed significantly in air quality prediction. In this paper, we go one step further and present a neat scheme of masked autoencoders, termed as masked air modeling (MAM), for sequence data self-supervised learning, which addresses the challenges posed by missing data. Specifically, the front end of our pipeline integrates a WRF-CAMx numerical model, which can simulate the process of emission, diffusion, transformation, and removal of pollutants based on atmospheric physics and chemical reactions. Then, the predicted results of WRF-CAMx are concatenated into a time series, and fed into an asymmetric Transformer-based encoder–decoder architecture for pre-training via random masking. Finally, we fine-tune an additional regression network, based on the pre-trained encoder, to predict ozone (O 3) concentration. Coupling these two designs enables us to consider the atmospheric physics and chemical reactions of pollutants while inheriting the long-range dependency modeling capabilities of the Transformer. The experimental results indicated that our approach effectively enhances the WRF-CAMx model’s predictive capabilities and outperforms pure supervised network solutions. Overall, using advanced self-supervision approaches, our work provides a novel perspective for further improving air quality forecasting, which allows us to increase the smartness and resilience of the air prediction systems. This is due to the fact that accurate prediction of air pollutant concentrations is essential for detecting pollution events and implementing effective response strategies, thereby promoting environmentally sustainable development.

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process.

In March 2022, a new wave of COVID-19 outbreak occurred in Shanghai due to the widespread transmission of the Omicron variant. A two-month citywide lockdown was implemented from April 1st to May 31st, adopting measures such as zone-based classification and grid management. This unique social event provided an “ideal air quality experiment” for pollution research. The rapid reduction in economic activities during the lockdown had many positive impacts on the environment, leading to overall improvements in air quality. Particularly, the concentration of NOx, one of the precursors to O3, significantly decreased. However, O3, as a typical secondary pollutant, showed a noticeable increase. This study uses the WRF-CAMx-OSAT air quality model method to analyze the source of O3 pollution in Shanghai from April to May 2022. The impact of O3 precursor control, sector sources, and regional contributions on the formation of O3 pollution in Shanghai is analyzed in depth. During the pandemic lockdown period, it was found that, in Shanghai, the overall O3 levels were controlled by VOCs (Volatile Organic Compounds), and controlling VOCs proved to be an effective measure in reducing O3 concentrations in Shanghai. Compared with the same period in 2021, the proportion of road traffic sources contributing to ozone concentration has significantly decreased from 70.61% to 64.3%, but they are still the largest contributor. The contribution of industrial emissions to the ozone concentration has significantly risen from 20.71% to 26.36%, making them still the second largest contributor. Industrial and traffic sources are emission sources that require particular attention. The contribution ratio of local sources to external transport is about 7:3, which is higher than the ratio of local sources to external transport in the same period of 2021, which is about 6:4. The local ozone is the main source of ozone concentration in Shanghai, and controlling local source emissions is the key to controlling ozone concentration in the Shanghai area. When excluding the impact of long-range transport, the main areas contributing to O3 formation from local sources are Baoshan District, Jiading District, Qingpu District, and Chongming District, accounting for approximately 41.12% of the total absolute contribution. Different source regions exhibit significant spatial variations in their contributions to the ozone concentration. Through these studies, we aim to provide scientific support and control suggestions for the precise prevention and control of O3 pollution in Shanghai.

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.

This paper showed different plume behaviors of exhausts from different number of stacks of the container ship, using CFD code PHOENICS version 6.0. The plume behavior was quantitatively analyzed by mass fraction of the pollutant in the exhaust and plume heights. Three simplified typical configurations were constructed by CFD according to the investigation of container ships. The configurations included a single main stack (BL1), one main stack and multiple auxiliary stacks (BL2), and two main stacks and multiple auxiliary stacks (BL3). All the main stacks had the same emission characteristics, and all the auxiliary stacks had the same emission characteristics. The results show that the transmission and diffusion characteristics of the exhaust from multiple stacks are different from those of the exhaust from a single stack. In BL2 and BL3 simulations, the maximum mass fraction of SO2 in the exhaust (C1max) of multiple stack emissions was approximately 329% and 269% higher than that of single stack emissions over the main stack, respectively, and the plume height of multiple stack emissions is higher than that of single stack emissions. In BL2 and BL3 simulations, the plume height of multiple stack emissions was 41% and 75% higher than that of single stack emissions, respectively. The increase of C1max, due to multiple stack emissions, is weakened as the distance of the stacks increase. The difference in plume behavior between multiple stack emissions and single stack emissions is of great significance for air quality management and pollution control in port areas.

About 75% energy demand and emissions all concentrate in urban areas, especially in the metropolises, placing a heavy burden on both the energy supply system and the environment system. To explore low emission pathways and provide policy recommendations for the Shanghai energy system and the environmental system to reach the carbon dioxide (CO2) peak by 2030 and attain emission reduction targets for local air pollutants (LAPs), a regional energy–environment optimization model was developed in this study, considering system costs, socio-economic development and technology. To verify the reliability of the model simulation and evaluate the model risk, a historical scenario was defined to calculate the emissions for 2004–2014, and the data were compared with the bottom-up emission inventory results. By considering four scenarios, we simulated the energy consumption and emissions in the period of 2020–2030 from the perspective of energy policies, economic measures and technology updates. We found that CO2 emissions might exceed the amount of 250 million tons by the end of 2020 under the current policy, and carbon tax with a price of 40 CNY per ton of carbon dioxide is an imperative measure to lower carbon emissions. Under the constraints, the emissions amount of SO2, NOx, PM10, and PM2.5 will be reduced by 95.3–180.8, 207.8–357.1, 149.4–274.5, and 59.5–119.8 Kt in 2030, respectively.

In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOX, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter.

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.

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.

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.

Many researchers have failed to utilize back-calculation to estimate traffic emissions effectively or have obtained unclear results. In this study, the back-calculation of traffic-related PM10 emission factors based on roadside concentration measurements was analyzed. Experimental conditions were considered to ensure the success of back-calculation. Roadside measurements were taken in a street canyon in Shanghai, China. Concentrations from a background site were often found to exceed the measured concentrations at the roadside on polluted days as more errors occurred in the background concentrations. On clean days, these impacts were negligible. Thus, only samples collected on clean days were used in back-calculation. The mean value from back-calculation was 0.138 g/km, which was much smaller than the results obtained using official emission models. Emission factors for light-duty vehicles (LDV), medium-duty vehicles (MDV), heavy-duty vehicles (HDV), and motorcycles were approximately 0.121, 0.427, 0.445, and 0.096 g/km, respectively. The fleet-averaged non-exhaust emission factor was approximately 0.121 g/km, indicating that road dust accounted for 87.7% of the roadside concentration increments. According to the dispersion simulation of reserved samples, the concentrations simulated using back-calculated emission factors were in better agreement with the measured data than the concentrations derived using modeled emission factors.