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Aerosol acidity is a critical factor affecting atmospheric chemistry. Here, we present a study on annual, monthly, and daily variations in PM2.5 pH in Shanghai during 2010–2020. With the effective control of SO2 emissions, the NO2/SO2 ratio increased from 1.26 in 2010 to 5.07 in 2020 and the NO3−/SO42− ratio increased from 0.68 to 1.49. Aerosol pH decreased from 3.27 in 2010 to 2.93 in 2020, regardless of great achievement in reducing industrial SO2 and NOx emissions. These findings suggest that aerosol acidity might not be significantly reduced in response to the control of SO2 and NOx emissions. The monthly variation in pH values exhibited a V-shape trend, mainly attributable to aerosol compositions and temperature. Atmospheric NH3 plays the decisive role in buffering particle acidity, whereas Ca2+ and K+ are important acidity buffers, and the distinct pH decline during 2010–2016 was associated with the reduction of Ca2+ and K+ while both temperature and SO42− were important drivers in winter. Sensitivity tests show that pH increases with the increasing relative humidity in summer while it is not sensitive to relative humidity in winter due to proportional increases in Hair+ and aerosol liquid water content (ALWC). Our results suggest that reducing NOx emissions in Shanghai will not significantly affect PM2.5 acidity in winter.

As “fuel” for atmospheric photochemical reactions, volatile organic compounds (VOCs) play a key role in the secondary generation of ozone (O3) and fine particulate matter (PM2.5, an aerodynamic diameter ≤ 2.5 μm). To determine the characteristics of VOCs in a high-level ozone period, comprehensive monitoring of O3 and its precursors (VOCs and NOx) was continuously conducted in an industrial area in Shanghai from 18 August to 30 September 2021. During the observation period, the average concentration of VOCs was 47.33 ppb, and alkanes (19.64 ppb) accounted for the highest proportion of TVOCs, followed by oxygenated volatile organic compounds (OVOCs) (13.61 ppb), alkenes (6.92 ppb), aromatics (4.65 ppb), halogenated hydrocarbons (1.60 ppb), and alkynes (0.91 ppb). Alkenes were the predominant components that contributed to the ozone formation potential (OFP), while aromatics such as xylene, toluene, and ethylbenzene contributed the most to the secondary organic aerosol production potential (SOAFP). During the study period, O3, NOx, and VOCs showed significant diurnal variations. Industrial processes were the main source of VOCs, and the second largest source of VOCs was vehicle exhaust. While the largest contribution to OFP was from vehicle exhaust, the second largest contribution was from liquid petroleum gas (LPG). High potential source contribution function (PSCF) values were observed in western and southeastern areas near the sampling sites. The results of a health risk evaluation showed that the Hazard Index was less than 1 and there was no non-carcinogenic risk, but 1,3-butadiene, benzene, chloroform, 1,2-dibromoethane, and carbon tetrachloride pose a potential carcinogenic risk to the population.

Shanghai, one of China’s largest metropolises, faces significant environmental pollution challenges due to rapid economic development. Suburban areas of Shanghai are affected by both long-distance transport and local sources of pollutants. This study conducted an integrated analysis that links health-risk assessment of heavy metals and source apportionment of atmospheric constituents to distinguish the contributions of emission sources and the major sources of health risks. Source-apportionment analysis revealed that secondary sources had the greatest contribution to the local pollutants, indicating the significant influence of peripheral and long-distance transport. Health-risk assessment of Cr, Ni, As, and Cd revealed that local residents were exposed to respiratory health risks, in which Cr is the major contributor. This health risk was primarily associated with emissions from nearby industry-related sources. Our study highlights the significant effects of both long-distance transport and local source emissions on atmospheric composition and human health in large urban agglomerations. The findings can inform future efforts to develop more precise emission-reduction strategies and policy improvements to mitigate environmental pollution and protect public health.

Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), and PM2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80–100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO2 higher in motorway concentrations, and PM2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO2, and PM2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices.

Among a series of 3D printing techniques, stereolithography provides a new route to produce ceramic architectures with the advantages of high-precision and short cycle time. However, up to now the stereolithography of non-oxide ceramics still face complex and difficult problems. This work focused on the analysis of rheological and curing ability of Si3N4 photocurable slurries. The effects of monomer type, coarse silicon powder, solid loading and ambient temperature on the rheological behavior were intensively studied. The relationships between powder characteristic (involving refractive index, absorbance and the introduce of coarse silicon powder), monomer type and curing ability were discussed in detail. It is expected that this study may benefit the development of Si3N4 or other non-oxide ceramic slurries for stereolithography.

Ground-level ozone (O3) pollution is still one of the priorities and challenges for air pollution control in the Yangtze River Delta (YRD) region of China. Understanding the relationship of O3 with its precursors and contributions of different sources in O3 formation is essential for the development of an O3 control strategy. This study analyzed O3 sensitivity to its precursors using a box model based on online observations of O3, non-methane hydrocarbons (NMHCs), nitrogen oxides (NOx), and carbon monoxide (CO) at an urban site and a suburban site in Shanghai in July 2017. Anthropogenic sources of NMHCs were identified using the positive matrix factorization (PMF) receptor model, and then contributions of different sources in O3 formation were estimated by the observation-based model (OBM). The relative incremental reactivity (RIR) values calculated by the OBM suggest that O3 formation at the urban site was in the NMHC-limited regime, while O3 formation at the suburban site tended between the transition regime and the NMHC-limited regime. Vehicular emission and liquefied petrochemical gas (LPG) use or aged air mass were found to be the two largest contributors at the urban and suburban sites in July, followed by paint and solvent use, and the petrochemical industry. However, from the perspective of O3 formation, vehicular emission and paint and solvent use were the largest two contributors at two sites due to the higher RIR values for paint and solvent use. In addition, the influence of transport on O3 sensitivity was identified by comparing O3 sensitivity at the suburban site across two days with different air mass paths. The result revealed that O3 formation in Shanghai is not only related to local emissions but also influenced by emissions from neighboring provinces. These findings on O3−NMHC−NOX sensitivity, contributions of different sources in O3 formation, and influence of transport could be useful for O3 pollution control in the YRD region. Nevertheless, more quantitative analyses on transport and further evaluation of the uncertainty of the OBM are still needed in future.

Instruments based on light scattering used to measure total suspended particulate (TSP) concentrations have the advantages of fast response, small size, and low cost compared to the gravimetric reference method. However, the relationship between scattering intensity and TSP mass concentration varies nonlinearly with both environmental conditions and particle properties, making it difficult to make corrections. This study applied four machine learning models (support vector machines, random forest, gradient boosting regression trees, and an artificial neural network) to correct scattering measurements for TSP mass concentrations. A total of 1141 hourly records of collocated gravimetric and light scattering measurements taken at 17 urban sites in Shanghai, China were used for model training and validation. All four machine learning models improved the linear regressions between scattering and gravimetric mass by increasing slopes from 0.4 to 0.9−1.1 and coefficients of determination from 0.1 to 0.8−0.9. Partial dependence plots indicate that TSP concentrations determined by light scattering instruments increased continuously in the PM2.5 concentration range of ~0−80 µg/m3; however, they leveled off above PM10 and TSP concentrations of ~60 and 200 µg/m3, respectively. The TSP mass concentrations determined by scattering showed an exponential growth after relative humidity exceeded 70%, in agreement with previous studies on the hygroscopic growth of fine particles. This study demonstrates that machine learning models can effectively improve the correlation between light scattering measurements and TSP mass concentrations with filter-based methods. Interpretation analysis further provides scientific insights into the major factors (e.g., hygroscopic growth) that cause scattering measurements to deviate from TSP mass concentrations besides other factors like fluctuation of mass density and refractive index.