Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Identification of potential source regions and long-range transport routes/channels of marine PM2.5 at remote sites in East Asia

Long-range transport (LRT) of air masses in East Asia and their impacts on marine PM2.5 were explored. Situated in the leeward region of East Asia, Taiwan Island marked by its elevated Central Mountain Range (CMR) separates air masses into two distinct air currents. This study aims to investigate the transport of PM2.5 from the north to the leeward region. Six transport routes (A-F) were identified and further classified them into three main channels (i.e. East, West, and South Channels) based on their transport routes and potential sources. Green Island (Site GR) and Hengchun Peninsula (Site HC) exhibited similarities in their transport routes, with Central China, North China, and Korean Peninsula being the major source regions of PM2.5, particularly during the Asian Northeastern Monsoons (ANMs). Dongsha Island (Site DS) was influenced by both Central China and coastal regions of East China, indicating Asian continental outflow (ACO) as the major source of PM2.5. The positive matrix factorization (PMF) analysis of PM2.5 resolved that soil dust, sea salts, biomass burning, ship emissions, and secondary aerosols were the major sources. Northerly Channels (i.e. East and West Channels) were primarily influenced by ship emissions and secondary aerosols, while South Channel was dominated by oceanic spray and soil dust. The results of W-PSCF and W-CWT analysis indicated that three remote sites experienced significant contributions from Central China in the highest PM2.5 concentration range (75-100%). In contrast, PM2.5 in the 0-25% and 25-50% ranges primarily originated from the open seas, with ship emissions being the prominent source. It suggested that northern regions with heavy industrialization and urbanization have impacts on high PM2.5 concentrations, while open seas are the main sources of low PM2.5 concentrations.

VOC source apportionment: How monitoring characteristics influence positive matrix factorization (PMF) solutions

Positive matrix factorization (PMF) can be used to develop more targeted air quality mitigation strategies by identifying major sources of a pollutant in an area. This technique is dependent, however, on the ability of PMF to resolve factors that accurately represent all sources of that pollutant in an area. We investigated how the accuracy of PMF solutions might be influenced by monitoring data characteristics, such as temporal resolution, monitoring location, and species composition, to better inform the use of PMF in VOC mitigation strategies. We applied PMF to five VOC monitoring programs collected within a four-year period in Colorado and found generally consistent factors, which we identified as oil extraction, processing, and evaporation; natural gas; vehicle exhaust; and liquid gasoline/short-lived oil and gas. The main determinant influencing whether or not a dataset resolved each of these sources was whether the dataset had a comprehensive list of VOC species covering key species of each source. Pollution spikes were not well-modeled in any of the solutions. Hyperlocal and volatile chemical product factors expected to be resolved in the industrialized, urban location were also missing, highlighting three limitations of PMF analysis. Wind direction dependence and diurnal trends aided in source identification, suggesting that high-time resolution data is important for developing actionable PMF results. Based on these findings, we recommend that air monitoring for PMF-informed VOC mitigation efforts include high temporal resolution and a comprehensive array of VOC species.

Mineralogy and phase transition mechanisms of atmospheric mineral particles: Migration paths, sources, and volatile organic compounds

Inorganic mineral particles play an important role in the formation of atmospheric aerosols in the Sichuan Basin. Atmospheric haze formation is accompanied by the phase transition of mineral particles under high humidity and stable climatic conditions. Backward trajectory analysis was used in this study to determine the migration trajectory of atmospheric mineral particles. Furthermore, Positive matrix factorization (PMF) was used to analyze the sources of atmospheric mineral particles. The phase transition mechanisms of atmospheric mineral particles were studied using ion chromatography, inductively coupled plasma emission spectrometry, total organic carbon analysis, X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy coupled with energy dispersive spectrometry, and grand canonical Monte Carlo methods. Three migration and phase transition paths were identified for the mineral particles. Sources of atmospheric mineral particles included combustion, vehicle emissions, industrial emissions, agricultural sources, and mineral dust. The main mineral phases in atmospheric particles, calcite and dolomite, were transformed into gypsum, and muscovite may be transformed into kaolinite. The phase transition of mineral particles seriously affects the formation of aerosols and worsens haze. Typically, along the Nanchong-Suining-Neijiang-Zigong-Yibin path, calcite is converted into gypsum under the influence of man-made inorganic pollution gases, which worsen the haze conditions and cause slight air pollution for 3-5 days. However, along the Guangyuan-Mianyang-Deyang-Chengdu-Meishan-Ya'an path, anthropogenic volatile organic compounds (VOCs) hindered gypsum formation from dolomite. Furthermore, dolomite and VOCs formed stable adsorption systems (system energies from -0.41 to -4.76 eV, long bonds from 0.20 to 0.24 nm). The adsorption system of dolomite and m/p-xylene, with low system energy (-1.46 eV/-1.33 eV) and significant correlation (r² = 0.991, p < 0.01), was the main cause of haze formation. Consequently, calcite gypsification and dolomite-VOC synergism exacerbated regional haze conditions. This study provides a theoretical reference for the mechanism of aerosol formation in basin climates.