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Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world's busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port's interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to explore the effects of COVID-19 control measures on the concentration and composition of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), and to better understand the sources of PM2.5-bound PAHs, PM2.5, samples were collected at two sites in urban and suburban areas of Shanghai before the lockdown, during the lockdown, after the lockdown in 2020, and during the same periods in 2019. The mass concentrations of 21 individual PAHs were determined via GC-MS analysis. While the COVID-19 control measures significantly reduced the absolute concentration of PM2.5-bound PAHs, they had no significant effect on their relative abundances, indicating that the significantly reduced traffic emission may not originally be the major source of PAHs in Shanghai. The differences in the composition of PM2.5-bound PAHs at three different lockdown-related periods may be caused by the gas-particle distribution of semi-volatile PAHs. The similarity in the composition of PM2.5-bound PAHs in different functional areas and different periods brings more uncertainties to the identification of PAH sources using the diagnostic ratios. During the lockdown period, the toxic equivalent concentration of PM2.5-bound PAHs in Shanghai was estimated to decrease by about 1/4, which still exhibits substantial carcinogenic risk upon exposure via inhalation. [ABSTRACT FROM AUTHOR]

Organic acids are important aerosol compositions with significant implications on particle formation, growth, acidity, phase state, and environmental impacts. Oxalic acid was found to be the most abundant particulate organic diacid in Shanghai during the study period, accounting for ~58% of the total dicarboxylic acids (C2–C10). Biomass burning (BB) explained a small but non-negligible fraction (less than 10%) of oxalate. Significant correlations between oxalate and sulfate indicated a potentially synergistic formation mechanism of oxalate and sulfate. In addition, meteorological factors such as ambient temperature and relative humidity were found to influence the formation of oxalate. Higher oxalate relative to inorganic particulate content was found in summer. Potential source contribution function analysis suggested that most of the oxalate observed in Shanghai was produced locally. The formation of oxalate was largely impacted by atmospheric oxidation capacity as indicated by its significant correlations with both secondary organic carbon (SOC) and sulfur oxidation ratio (SOR). The evolution of oxalate, oxalate/sulfate, oxalate/organic carbon were consistent with the emission trend of volatile organic carbons (VOCs) in recent years, indicating that oxalate may be derived from secondary oxidation of VOCs, which is further confirmed by a positive relationship between Ox and oxalate/VOCs over the study period. With a detailed characterization of oxalate in Shanghai, our study highlights the importance of regulating primary emissions, such as VOCs, as well as mitigation of atmospheric oxidation capacity in controlling air pollution in a coastal megacity. [ABSTRACT FROM AUTHOR]

In order to explore the mechanism of haze formation, the meteorological effect and chemical reaction process of the explosive growth (EG) of PM2.5 were studied. In this study, the level of PM2.5, water-soluble inorganic ions, carbonaceous aerosols, gaseous precursors, and meteorological factors were analyzed in Shanghai in 2018. The EG event is defined by a net increase of PM2.5 mass concentration greater than or equal to 100 μg m−3 within 3, 6, or 9 h. The results showed that the annual average PM2.5 concentration in Shanghai in 2018 was 43.2 μg m−3, and secondary inorganic aerosols and organic matter (OM) accounted for 55.8% and 20.1% of PM2.5, respectively. The increase and decrease in the contributions of sulfate, nitrate, ammonium (SNA), and elemental carbon (EC) to PM2.5 from clean days to EG, respectively, indicated a strong, secondary transformation during EG. Three EG episodes (Ep) were studied in detail, and the PM2.5 concentration in Ep3 was highest (135.7 μg m−3), followed by Ep2 (129.6 μg m−3), and Ep1 (82.3 μg m−3). The EG was driven by stagnant conditions and chemical reactions (heterogeneous and gas-phase oxidation reactions). This study improves our understanding of the mechanism of haze pollution and provides a scientific basis for air pollution control in Shanghai. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (NO 2 ), and PM 2.5 , 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, NO 2 higher in motorway concentrations, and PM 2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO 2 , and PM 2.5 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.