Evaluation of air quality changes in a Chinese megacity over a 15-year period (2006-2021) using PM2.5 receptor modelling

Air quality impairment has a massive impact on human health, with atmospheric particulate matter (PM) playing a major role. The People's Republic of China experienced a trend of increasing PM2.5 concentrations from 2000 to 2013. However, after the application of the Air Pollution Prevention and Control Action Plan and other related control measures, sharp decreases in air pollutant concentrations were particularly evident in the city of Wuhan (central China). This study analysed major changes in PM2.5 concentrations, composition and source apportionment (using receptor modelling) based on Wuhan's PM2.5 chemical speciation datasets from 2006 to 2007, 2019-2021 and contemporaneous gaseous pollutant values. Average SO2 concentrations decreased by 88%, from the first to the second period, mostly due to measures that reduced coal combustion. However, NO2 only declined by 25%, with policy measures likely being undermined by an increased number of vehicles. PM2.5 concentrations decreased by 65%, with the PM constituents each being affected differently. Coal combustion-related element concentrations, OC, SO42-, NH4+, EC, Cl-, Al, Ca, Cu, Fe, Co and NO3- decreased by 22-90%. Secondary inorganic aerosol (SIA) was initially dominated by (NH4)2SO4 (73%) in 2006, but later dominated by NH4NO3 (52%) in 2021. Receptor modelling identified major sources contributing to PM2.5: Mineral, road and desert dust (MRDD), Secondary sulphate (SECS), Secondary nitrate (SECN), Tungsten industry (W), Toxic Elements of Coal (TEC), Iron and Steel (IRONS), Coal Combustion (CC), Residential Heating (RH), Refinery (REF) and Traffic (TRF). In relative proportions, TEC (-83%), SECS (-64%) and SECN (-48%) reduced their contributions to PM2.5 whilst MRDD increased (+62.5%). Thus, the results indicate not only a drastic abatement of PM pollution in Wuhan but also a change in the sources of pollution, which requires further actions to reduce PM2.5 concentrations to health protective values. Secondary PM and fugitive emissions are key components to abate.

Volatile organic compounds and their contribution to ground-level ozone formation in a tropical urban environment

This study aims to determine the trends of volatile organic compound (VOC) concentrations and their potential contribution to O₃ formation. The hourly data (August 2017 to July 2018) for 29 VOCs were obtained from three Malaysian Department of Environment continuous air quality monitoring stations with different urban backgrounds (Shah Alam, Cheras, Seremban). The Ozone Formation Potential (OFP) was calculated based on the individual Maximum Incremental Reactivity (MIR) and VOC concentrations. The results showed that the highest mean total VOC concentrations were recorded at Cheras (148 ± 123 μg m^-3), within the Kuala Lumpur urban environment, followed by Shah Alam (124 ± 116 μg m^-3) and Seremban (86.4 ± 89.2 μg m^-3). VOCs such as n-butane, ethene, ethane and toluene were reported to be the most abundant species at all the selected stations, with overall mean concentrations of 16.6 ± 11.9 μg m^-3, 12.1 ± 13.3 μg m^-3, 10.8 ± 11.9 μg m^-3 and 9.67 ± 9.00 μg m^-3, respectively. Alkenes (51.3-59.1%) and aromatic hydrocarbons (26.4-33.5%) have been identified as the major contributors to O₃ formation in the study areas based on the overall VOC measurements. Relative humidity was found to influence the concentrations of VOCs more than other meteorological parameters. Overall, this study will contribute to further understanding of the distribution of VOCs and their contribution to O₃ formation, particularly in the tropical urban environment.

Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and "other sources". From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to "other sources" and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.

Direct target and non-target analysis of urban aerosol sample extracts using atmospheric pressure photoionisation high-resolution mass spectrometry

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous atmospheric pollutants of high concern for public health. In the atmosphere they undergo oxidation, mainly through reactions with ·OH and NOx to produce nitro- and oxygenated (oxy-) derivatives. In this study, we developed a new method for the detection of particle-bound PAHs, nitro-PAHs and oxy-PAHs using direct infusion into an atmospheric pressure photoionisation high-resolution mass spectrometer (APPI-HRMS). Method optimisation was done by testing different source temperatures, gas flow rates, mobile phases and dopants. Samples were extracted with methanol, concentrated by evaporation and directly infused in the APPI source after adding toluene as dopant. Acquisition was performed in both polarity modes. The method was applied to target analysis of seasonal PM_2.5 samples from an urban background site in Padua (Italy), in the Po Valley, in which a series of PAHs, nitro- and oxy-PAHs were detected. APPI-HRMS was then used for non-target analysis of seasonal PM_2.5 samples and results compared with nano-electrospray ionisation (nanoESI) HRMS. The results showed that, when samples were characterised by highly oxidised organic compounds, including S-containing compounds, like in summer samples, APPI did not bring any additional information with respect to nanoESI in negative polarity (nanoESI(-)). Conversely, for winter samples, APPI(-) could detect a series of aromatic and poly-aromatic compounds, mainly oxidised and nitrogenated aromatics, that were not otherwise detected with nanoESI.

Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China

Air pollution episodes in China are frequent and a more comprehensive understanding of pollution sources and impacts is needed to design appropriate strategies and set emission reduction targets. This study analyzes PM2.5 and PM10 concentrations measured in 23 cities at 178 urban sites and at 23 corresponding "urban contrast" sites in China with the goals of understanding spatial and temporal trends and quantifying the regional component of PM pollution. The contrast sites, located an average of 29km from cities in the upwind direction, are intended to represent "background" levels. Using daily measurements from April 2013 to March 2014, we assess compliance with air quality standards, PM2.5/PM10 ratios and urban "increments," defined as the increase in PM levels in the city compared to the contrast site. Spatial and temporal patterns at daily, monthly and annual levels are shown using distributions, correlations, spatial autocorrelation, and factor analyses. At the contrast sites, PM2.5 and PM10 concentrations averaged 56±26 and 91±44μgm-3, respectively, and China's daily and annual average air quality standards were frequently exceeded. PM2.5 and PM10 concentrations in most cities exceeded levels at the corresponding contrast sites, but by an average of only 14±14 and 26±27μgm-3, respectively. Seasonal changes in PM2.5 and PM10 concentrations and urban increments were striking, e.g., levels increased 2 to 3-fold in winter at several sites. The significance of exurban and regional sources of PM2.5 is demonstrated by the small urban increments, the strong correlations across broad regions, and the correlation between daily levels at city and contrast sites. These sources will require control to achieve air quality goals, in particular, the PM10 and PM2.5 targets announced by the Chinese government in 2013.

Mass and number size distributions of particulate matter components: comparison of an industrial site and an urban background site

Size-resolved composition of particulate matter (PM) sampled in the industrial town of Port Talbot (PT), UK was determined in comparison to a typical urban background site in Birmingham (EROS). A Micro-Orifice Uniform Deposit Impactor (MOUDI) sampler was deployed for two separate sampling campaigns with the addition of a Grimm optical spectrometer at the PT site. MOUDI samples were analysed for water-soluble anions (Cl(-), NO₃(-) and SO₄(2-)) and cations (Na(+), NH4(+), K(+), Mg(2+) and Ca(2+)) and trace metals (Al, V, Cr, Mn, Fe, Cu, Zn, Sb, Ba and Pb). The PM mass distribution showed a predominance of fine particle (PM₂.₅) mass at EROS whereas the PT samples were dominated by the coarse fraction (PM₂.₅₋₁₀). SO₄(2-), Cl(-), NH4(+), Na(+), NO₃(-), and Ca(2+) were the predominant ionic species at both sites while Al and Fe were the metals with highest concentrations at both sites. Mean concentrations of Cl(-), Na(+), K(+), Ca(2+), Mg(2+), Cr, Mn, Fe and Zn were higher at PT than EROS due to industrial and marine influences. The contribution of regional pollution by sulphate, ammonium and nitrate was greater at EROS relative to PT. The traffic signatures of Cu, Sb, Ba and Pb were particularly prominent at EROS. Overall, PM at EROS was dominated by secondary aerosol and traffic-related particles while PT was heavily influenced by industrial activities and marine aerosol. Profound influences of wind direction are seen in the 72-hour data, especially in relation to the PT local sources. Measurements of particle number in 14 separate size bins plotted as a function of wind direction and speed are highly indicative of contributing sources, with local traffic dominant below 0.5 μm, steelworks emissions from 0.5 to 15 μm, and marine aerosol above 15 μm.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies.