A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM_2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) μg m^-3, respectively. The highest PM_2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM_2.5, OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 μg m^-3, respectively, while the meteorological contributions were 5.38, 2.49, and -0.62 μg m^-3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality.

Enhanced nitrate contribution to light extinction during haze pollution in Chengdu: Insights based on an improved multiple linear regression model

In recent years, the annual mean concentration of PM_2.5 has decreased in Chengdu, China; however, atmospheric visibility has not improved accordingly. Low-visibility events occurred even when the PM_2.5 mass concentrations were below the national ambient air quality secondary standard (daily mean concentration, 75 μg/m³). In this study, the non-linear relationship between PM_2.5 and visibility was analyzed under different NO₃^- mass fractions in PM_2.5 based on 2-year field observation data. The results indicated that NO₃^- formation contributed to particulate pollution events and reduced atmospheric visibility. Multiple linear regression was used to propose a localized reconstruction equation for the light-scattering coefficient. According to the maximum likelihood estimation method and log-transformed residuals, the mass scattering coefficients (MSEs) of organic matter (OM), NH₄NO₃, and (NH₄)₂SO₄ in Chengdu were 7.42, 3.83, and 3.80, respectively. OM and NH₄NO₃ contributed to more than 50% of the light-extinction coefficient (b_ext). NH₄NO₃ was the main pollutant causing the substantial increase in b_ext. Chengdu has a high relative humidity (annual mean 70%), and under such conditions, the contribution of NH₄NO₃ to b_ext was considerably enhanced through hygroscopic growth and heterogeneous reactions. This study estimated the localized MSEs of OM, NH₄NO₃, and (NH₄)₂SO₄ in Chengdu and emphasized that effective control measures to reduce nitrate and its precursors could simultaneously ameliorate air quality and visibility in humid regions with poor atmospheric visibility.

Impacts of Human Activities and Climate Change on Water Storage Changes in Shandong Province, China

The over-exploitation of water resources causes water resource depletion, which threatens water security, human life, and social and economic development. Only by clarifying the spatial pattern, changing trends, and influencing factors of water storage can we promote the rational development of water resources and relieve the pressure on water resources. However, there is still a lack of research on these aspects. In this study, the water-scarce area in Shandong Province, China, was selected to quantify the spatial and temporal changes in the terrestrial water storage (TWS) and groundwater storage (GWS) over the past 30 years. Nighttime light data were used to characterize the urbanization level (UL) and explore the effects of human activities (i.e., UL) and climate change (temperature and precipitation) on the TWS and GWS. The results show that 1) from 1990 to 2018, the overall TWS exhibited a significant decreasing trend (- 0.084 cm yr^-1). The change trend of the GWS was consistent with that of the TWS (- 0.516 m³ yr^-1). Spatially, there was significant spatial heterogeneity in the trend of the TWS and GWS. At the grid and prefectural scales, the TWS mainly exhibited a downward trend in the central and western regions, and an upward trend in the eastern region of Shandong Province. For the GWS, all cities exhibited a decreasing trend at the prefectural scale, whereas 92% of the regions exhibited a decreasing trend with less spatial heterogeneity at the grid scale. 2) Precipitation was the mean factor controlling the total amount of TWS and GWS in Shandong Province. Precipitation and temperature positively affected water storage, and the UL negatively affected it. At the prefectural scale, except for a few cities which were greatly influenced by the UL, the dominant factor of the TWS and GWS was precipitation in the other cities. At the grid scale, for the TWS, precipitation was the predominant factor in 51.82% of the entire region, followed by the UL (44.14%) and temperature (4.04%). For the GWS, precipitation was the predominant factor in 55.73% of the area, and the other 44.27% of the area was mainly influenced by the UL. Overall, precipitation and the UL were the key factors affecting the TWS and GWS. The results of this study provide a theoretical and decision-making basis for the optimal allocation and sustainable use of regional water resources.

A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data

Maximum likelihood (ML) estimators of the model parameters in multiple linear regression are obtained using genetic algorithm (GA) when the distribution of the error terms is long-tailed symmetric. We compare the efficiencies of the ML estimators obtained using GA with the corresponding ML estimators obtained using other iterative techniques via an extensive Monte Carlo simulation study. Robust confidence intervals based on modified ML estimators are used as the search space in GA. Our simulation study shows that GA outperforms traditional algorithms in most cases. Therefore, we suggest using GA to obtain the ML estimates of the multiple linear regression model parameters when the distribution of the error terms is LTS. Finally, real data of the Covid-19 pandemic, a global health crisis in early 2020, is presented for illustrative purposes.

Mechanisms for log normal concentration distributions in the environment

Log normal-like concentration distributions are ubiquitously observed in the environment. However, the mechanistic origins are not well understood. In this paper, we show that first order exponential kinetics onsets log-normal concentration distributions, under certain assumptions. Given the ubiquity of exponential kinetics, e.g., source and sink processes, this model suggests an explanation for the frequent observation in the environment, and elsewhere. We compare this model to other mechanisms affecting concentration distributions, e.g., source mixing. Finally, we discuss possible implications for data analysis and modelling, e.g., log-normal rates and fluxes.