Air Quality Atmosphere and Health最新文献

Abstract

In 2020, during the COVID-19 pandemic, containment measures were applied inducing potential changes in air pollutant concentrations and thus in air toxicity. This study evaluates the role of restrictions on biological effects of particulate matter (PM) in different Northwest Italy sites: urban background, urban traffic, rural, and incinerator. Daily PM samples collected in 2020 were pooled according to restrictions: January/February (no restrictions), March and April (first lockdown), May/June and July/August/September (low restrictions), October/November/December (second lockdown). The 2019 samples (pre-pandemic period) were pooled as 2020 for comparison. Pools were extracted with organic solvents and extracts were tested to assess cytotoxicity (WST-1 assay) and genotoxicity (comet assay) on BEAS-2B cells, mutagenicity (Ames test) on TA98 and TA100 Salmonella typhimurium strains, and estrogenic activity (gene reporter assay) on MELN cells. Pollutant concentrations were also analyzed (PM₁₀, PM_2.5, polycyclic aromatic hydrocarbons). No difference was observed for PM and polycyclic aromatic hydrocarbon concentrations between 2020 and 2019. During lockdown months (2020), PM cytotoxicity/genotoxicity was significantly lower in some sites than during 2019, while considering PM mutagenicity/estrogenic activity some differences were detected but without statistical significance. PM extract effects decreased in some sites during 2020; this may be due to lockdowns that reduced/modified pollutant emissions and may be related also to complex PM origin/formation and to meteorological conditions. In conclusion, the study confirms that PM biological effects cannot be assessed considering only the PM concentration and suggests to include a battery of bioassay for air quality monitoring in order to protect human health from air pollution effects.

Graphical Abstract

Abstract

The aim of this research is to analyze the main influencing factors and relationship between atmospheric environment and economic society. Using the panel data of 18 cities in Henan Province from 2006 to 2020, this paper employed some advanced econometric estimation included entropy method, extended environmental Kuznets curve (EKC) and STIRPAT model to conduct empirical estimations. The results show that most regions in Henan Province have verified the existence of the EKC hypothesis; and the peak of air pollution level in all cities of Henan Province generally occurred in around 2014. Multiple linear Ridge regression indicated that the positive driving forces of air pollution in most cities in Henan Province are industrial structure and population size; the negative driving forces are urbanization level, technical level and greening degree. Finally, we used the grey GM (1, 1) model to predict the atmospheric environment of Henan Province in 2025, 2030, 2035 and 2040. What should pay close attention to is that air pollution levels in northeastern and central Henan Province will continue to remain high.

Millions of people suffer from health problems due to poor air quality in regions of high particulate pollution. Therefore, this paper proposes for better understanding of the impact of dust storms on both short- and long-term environmental factors that can help in a preferable formulation of warning and prediction scenarios in arid regions. We evaluate the effect of dust storms on the optical properties of aerosols and meteorological parameters by employing both ground-based and satellite remote sensing approaches. For this, we use AERosol RObotic NETwork (AERONET), MODerate resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Ozone Monitoring Instrument (OMI), Suomi National Polar-orbiting Partnership (S-NPP), and Sentinel-5 Precursor (S5P) to retrieve aerosol optical properties from 15 May 2021 to 20 May 2021 over Karachi. At 550nm, the instantaneous maximum values of aerosol optical depth (AOD) are measured to be ~0.8, and 0.59 on 16 May 2021, and the lowest values of Ångström Exponent (AE) are discovered to be ~0.2, and ~0.26 by Aqua-MODIS and AERONET respectively. Such observations are attributed to dust aerosols over Karachi; these values are more than those that might be expected on an ordinary day. We also found S5P and OMI retrieved ultraviolet aerosol index (UVAI) of about 1 and ~1.9 on 17 May 2021 respectively which indicate the presence of absorbing (dust) aerosols. Subtypes of aerosols derived by CALIPSO with vertical profile taken on 17 May 2021 segregate the widespread aerosol burden as contaminated dust particles over surrounding regions of Karachi (24.48–30.59° N, 69.01–70.56° E).

A prompt and accurate prediction of air quality index (AQI) has become a necessity to tackle the mounting environmental threats. This paper proposes a feature-driven hybrid method for hourly, 3-step-ahead, and deterministic AQI prediction, which includes three modules. In Module 1, an “extract-merge-filter” procedure of feature engineering is created to capture the potential features from the AQI series. Ten feature sets are generated as candidates. In Module 2, six models including Light Gradient Boosting Machine, Extreme Gradient Boosting, Long Short-Term Memory, Convolutional Neural Network, Multilayer Perceptron, and Deep Neural Network are developed as base predictors and performed on the candidate features. In Module 3, predictors are first matched with their optimal features using a comprehensive metric, and then combined in an optimized ensemble using OPTUNA. A case study on the AQI data from four different Chinese cities is carried out to demonstrate the method. The experimental results show the following: (1) Feature engineering significantly boosts prediction performance and provides interpretable findings for practical use. (2) Customized input of features to the predictors is more effective than a fixed input and can rise the performance to a higher level. (3) OPTUNA is a promising tool for optimizing ensemble weights. The final ensemble model is superior to single machine learning models and has a good robustness.

Recent studies conducted in the USA have shown that adverse health effects of fine particulate matter (PM_2.5) persist at levels below the national air quality standards. More recently, particle radioactivity has also been associated with adverse health effects. However, the importance of particle radioactivity at low PM_2.5 levels has not been thoroughly explored. The present paper investigates the temporal trends and the relationship of particle gross β-activity (PM-β) and PM_2.5 mass in the 48 states of the contiguous USA during the period of 2001–2017. With the implementation of stringent air pollution control policies, national ambient PM_2.5 concentrations decreased by 38.5% during this period. However, a smaller decrease of 9.4% was observed for PM-β, while the mean PM-β/PM_2.5 ratio increased by 49.1%. PM-β is mostly associated with radon emissions and its progeny, which do not change much with time. The largest PM_2.5 and PM-β reductions were observed in the Southeast, while the smallest were found in the West. When the aggregated PM-β to PM_2.5 ratio is stratified by PM_2.5 levels, the ratio was found to be highest when PM_2.5 is <3 μg/m³, with a median PM-β to PM_2.5 ratio of 0.77 (0.64–0.88; 25th–75th percentiles). Overall, when not stratified by PM_2.5 levels, the greatest state-wide overall PM-β /PM_2.5 ratios were found in Wyoming (0.69) and South Dakota (0.51), areas with higher radon, while the lowest (0.17) were in Delaware followed by New Jersey (0.18). These results indicate that the ratio of ambient particle radioactivity to particle mass concentration typically is higher at low PM_2.5 levels, and consequently, the toxicity per unit mass is expected to be higher.

Climate change will have a great impact on thermal bioclimate conditions and thermal perceptions, and statistical downscaling methods may introduce uncertainties to climate projection which should be considered in future planning. Therefore, the present study aims to evaluate the statistical downscaling method of Cumulative Distribution Function–transform (CDF-t) in simulating the thermal comfort characteristics of Iran based on the physiologically equivalent temperature index (PET). The daily maximum and minimum temperature, wind speed, and relative humidity of ERA5 climate reanalysis as well as the observational data of 91 synoptic stations during 30-year period (1987–2017) were used. The model was validated using Pearson correlation, Kolmogorov–Smirnov test, climatic RMSE, and Moran’s I statistic. RMSE values (2–4.9) indicated a slight deviation of the simulated series compared to the observed PET of the stations. Correlation values of 0.6–1 indicate a linear and positive relationship between the comfort series. In addition, adherence to the same statistical distribution between the mentioned series was confirmed by Kolmogorov–Smirnov test. The results showed that the observed and simulated PET values were consistent in the nine climatic regions of Iran except for the temperate regions of dry and hot summers (Csb) as well as snowy climate with dry and hot summers (Dsb). Moran’s statistics at a 95% confidence level showed that this method is suitable for simulating the data without making any unexpected changes in the geographical distribution of it. In general, the CDF-t has a good performance in downscaling of the above-mentioned variables in this study area for future.

For best-informed decision-making to improve climate change adaptation and reduce present and future air pollution health hazards, it is essential to identify major trends in spatiotemporal air quality patterns of common air contaminants. This study examined the patterns and trends of SO₂, NO₂, CO, O₃, and particulate matter (PM) air pollutants over 91 monitoring stations in Egypt during 93 months in the August (2013)–April (2021) period. In situ data with their monthly, seasonal, and yearly spatial trends are defined and used to validate the counterpart satellite reanalysis MERRA-2 data. The Mann–Kendall test characterized the seasonal monotonic trends and their Sen’s slope, and annual change rate for both data series. Regression analysis of MERRA-2 against in situ concentrations of SO₂ and PM₁₀ revealed underestimation with RMSE values of 13.38 g m⁻³ and 69.46 g m⁻³, respectively. Local plumes with variable magnitudes characterized distinct industrial places clarified by patterns of in situ pollutants. As a result of the COVID-19 lockdown, the in situ air pollutants showed a considerable regional decline in the yearly average in 2020 compared to the years before. The in situ air pollutants showed annual trends far more significant than those seen in the MERRA-2 data. The shortcomings of the few and spatiotemporal discontinuities of the in situ contaminants are addressed by MERRA-2 air quality products. The in situ data made trends and magnitudes clear that were hidden in their MERRA-2 counterparts. The results clarified air pollution patterns, trends, and spatial variability over Egypt that are essential for climate risk management and for reducing environmental/health concerns.