Search Results (1,250)

Fine particulate matter (PM_2.5) pollution poses a major threat to human physical and mental health. Smart cities (SCs) provide innovative paths for PM_2.5 pollution prevention and control through Internet of Things (IoT) monitoring, intelligent transportation optimization, and other technological means. Based on the panel data of 2,141 counties in China between 2006 and 2021, this paper constructs a difference-in-differences with multiple time periods (MDID) to systematically assess the impact of SC on PM_2.5 concentration and analyze its mechanism of action by combining the satellite remote sensing PM_2.5 concentration (PM_2.5C) and the list of smart city pilots. This study finds the following: (1) SC significantly reduced the PM_2.5 concentration in the test area by about 3.58%. This conclusion was verified through rigorous robustness testing; (2) SC can effectively reduce PM_2.5C through the innovation effect; (3) High-quality economic development can strengthen the emission reduction effect of SC on PM_2.5C; (4) The environmental benefits of SC show significant spatial heterogeneity, with the largest PM_2.5 reductions occurring in the western regions (4.3% reduction), followed by regions with mature digital infrastructure and cities in high administrative level cities. The results of this study provide a reference for the regional differentiated implementation of the “14th Five-Year Plan for the Development of Innovative Smarter Cities”, and make targeted recommendations for the synergistic management of air quality under the “dual-carbon” goal. Full article

The combined pollution of fine particulate matter (PM_2.5) and ozone (O₃) is increasing synergistically on a global scale, posing a serious threat to human health. However, the joint toxicity and the underlying mechanisms associated with co-exposure to PM_2.5 and O₃ remain poorly understood. Through complementary in vivo animal models and in vitro cellular assays, the results demonstrate that although there was no synergistic cytotoxicity effect between PM_2.5 and O₃, the presence of O₃ significantly enhanced the genotoxicity of PM_2.5 by inducing severe DNA double-strand breaks. Furthermore, O₃ exposure significantly exacerbated the bioaccumulation of PM_2.5 by disturbing the cellular membrane integrity, thus leading to synergistic toxicity in bronchial cells and mouse lungs. Astaxanthin (AST) effectively antagonized the adverse effects of PM_2.5 and O₃ co-exposure by maintaining cell membrane integrity. These findings enhance our understanding of the pathophysiological mechanisms induced by co-exposure to PM_2.5 and O₃, and provide a promising therapeutic strategy for treating respiratory diseases caused by unavoidable exposure to these pollutants. Full article

This study quantifies the atmospheric deposition (AD) of nutrient loads into the Farmington Bay ecosystem via wet deposition over a three-year period. We analyzed nutrient concentrations from 509 total phosphorus (TP), 507 orthophosphate (OP), and 511 total nitrogen (TN) samples collected at seven locations around the Bay. We estimated AD loads using two different spatial interpolation methods, Kriging and Inverse Distance Weighting (IDW), as well as average concentrations. The loads computed using Kriging and IDW were similar, but the loads computed using sample averages were about 70% smaller. We estimated that annual atmospherically deposited nutrient loads range from 306 to 594 Mg for TN, 73 to 195 Mg for TP, and 43 to 144 Mg for OP. The loads in 2023 were significantly higher than those in 2021 and 2022, a phenomenon we attribute to higher precipitation and a major loading event that occurred on 13 April 2023. Based on comparison with studies concerning nearby Utah Lake, the total loads could be two to three times larger than our estimates. These studies suggest that fine particulate matter may significantly contribute to AD nutrient loads, but these loads are not captured by our sampling method. However, the inclusion of non-water surfaces in Farmington Bay may mitigate this difference. Full article

Industrial development has improved living standards; however, mortality associated with fine particulate matter (PM_2.5) exposure continues to rise. Despite increasing awareness of its health risks, effective strategies to mitigate PM_2.5-induced pulmonary damage remain limited. This study examines the protective properties of an ethanolic extract from Polygonum multiflorum (EPM) in preventing pulmonary dysfunction induced by PM_2.5, as well as its possible use as a dietary intervention to improve respiratory health. The physiological compounds in EPM were identified using ultra-performance liquid chromatography, and its protective effects were evaluated via in vitro assays using A549 and RPMI 2650 cells. The antioxidant system and mitochondrial function were further analyzed in the lung tissues of PM_2.5-exposed BALB/c mice, with molecular mechanisms elucidated by Western blot analysis. The main bioactive compounds identified in EPM included 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside. EPM modulated the Nrf2 signaling pathway, enhancing antioxidant defense by regulating the expression of antioxidant-related proteins. Furthermore, EPM exhibited protective effects against inflammation, apoptosis, and fibrosis through the TLR4/p-JNK and TGF-β1 signaling pathways. These findings suggest that EPM exerts protective effects against PM_2.5-induced oxidative stress and inflammation and may be used as a functional food ingredient for respiratory health. Full article

Prolonged exposure to fine particulate matter (PM_2.5) has been implicated in accelerated aging, including organ fibrosis. This study aimed to investigate whether prenatal and postnatal PM_2.5 exposure promotes renal fibrogenesis in adulthood and whether long-term vitamin D supplementation alleviates associated renal injury. Pregnant Sprague-Dawley rats were randomly assigned to three groups: control (normal saline, NS), PM_2.5 exposure, and PM_2.5 exposure with vitamin D supplementation during gestation and lactation (n = 3/group). Male offspring were subsequently exposed to the same conditions from postnatal weeks 3 to 8 (n = 7/group). On postnatal day 56, PM_2.5-exposed rats showed lower body weight and more severe glomerular and tubulointerstitial damage compared to controls. Serum calcium levels were elevated in the PM_2.5 group. The expression of intrarenal renin, transforming growth factor-β1, α-smooth muscle actin, and vimentin was upregulated, accompanied by increased collagen deposition. Long-term vitamin D supplementation reversed most of these changes, except for intrarenal vimentin expression and serum calcium levels. These findings indicate that prenatal and postnatal PM_2.5 exposure can activate intrarenal renin signaling and fibrogenic pathways, contributing to renal fibrosis later in life. Long-term vitamin D supplementation may provide partial protective effects against PM_2.5-induced renal fibrogenesis. Full article

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article

Environmental justice research in the United States (US) documents greater air pollution exposures for Hispanic/Latino vs. non-Hispanic White groups. EJ research has not focused on the intersection of race/ethnicity and older age nor short-term fine particulate matter (PM_2.5) exposures. We address these knowledge gaps by studying US metropolitan area census tracts within 100 km of the US–Mexico border, a region with serious air quality issues. We use US Census American Community Survey data to construct sociodemographic variables and Environmental Protection Agency Downscaler data to construct long-term and short-term measures of PM_2.5 exposure. Using multivariable generalized estimating equations, we test for differences in PM_2.5 exposures between census tracts with higher vs. lower proportions of older Hispanic/Latino residents and older non-Hispanic White residents. The results indicate that as the proportion of the Hispanic/Latino population ≥ 65 years of age increases, long-term and short-term PM_2.5 exposures significantly increase. In contrast, as the proportion of the non-Hispanic White population ≥ 65 years of age increases, changes in long-term and short-term PM_2.5 exposures are statistically non-significant. These findings illuminate how race/ethnicity and older age intersect in shaping PM_2.5 exposure disparities and may inform efforts to mitigate air pollution exposures for overburdened people along the US–Mexico border. Full article

In recent years, frequent open biomass burning (OBB) activities such as agricultural residue burning and forest fires have led to severe air pollution and carbon emissions across South and Southeast Asia (SSEA). We selected this area as our study area and divided it into two sub-regions based on climate characteristics and geographical location: the South Asian Subcontinent (SEAS), which includes India, Laos, Thailand, Cambodia, etc., and Equatorial Asia (EQAS), which includes Indonesia, Malaysia, etc. However, existing methods—primarily emission inventories relying on burned area, fuel load, and emission factors—often lack accuracy and temporal resolution for capturing fire dynamics. Therefore, in this study, we employed high-resolution fire point data from China’s Feng Yun-4A (FY-4A) geostationary satellite and the Fire Radiative Power (FRP) method to construct a daily OBB emission inventory at a 5 km resolution in this region for 2020–2022. The results show that the average annual emissions of carbon (C), carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), non-methane organic gases (NMOGs), hydrogen (H₂), nitrogen oxide (NO_X), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), total particulate matter (TPM), total particulate carbon (TPC), organic carbon (OC), black carbon (BC), ammonia (NH₃), nitric oxide (NO), nitrogen dioxide (NO₂), non-methane hydrocarbons (NMHCs), and particulate matter ≤ 10 μm (PM₁₀) are 178.39, 598.10, 33.11, 1.44, 4.77, 0.81, 1.02, 0.28, 3.47, 5.58, 2.29, 2.34, 0.24, 0.58, 0.43, 0.99, 1.87, and 3.84 Tg/a, respectively. Taking C emission as an example, 90% of SSEA’s emissions come from SEAS, especially concentrated in Laos and western Thailand. Due to the La Niña climate anomaly in 2021, emissions surged, while EQAS showed continuous annual growth at 16.7%. Forest and woodland fires were the dominant sources, accounting for over 85% of total emissions. Compared with datasets such as the Global Fire Emissions Database (GFED) and the Global Fire Assimilation System (GFAS), FY-4A showed stronger sensitivity and regional adaptability, especially in SEAS. This work provides a robust dataset for carbon source identification, air quality modeling, and regional pollution control strategies. Full article

Background: This study aimed to investigate the long-term effects of maternal exposure to fine particulate matter (PM_2.5) with or without vitamin D supplementation on the renal microvasculature in adult rat offspring. Methods: Pregnant Sprague–Dawley rats were exposed to normal saline, PM_2.5, and PM_2.5 with vitamin D for one month during nephrogenesis. Male offspring kidneys were taken for analyses on postnatal day 56. Results: Adult offspring rats exposed to maternal PM_2.5 exhibited lower body weights and greater glomerular and tubular injury scores compared to control rats. Semi-quantitative analysis revealed a significant reduction in glomerular and peritubular capillary endothelial cells, along with a decrease in the number of glomeruli in the PM_2.5 group. Maternal vitamin D supplementation reduced these changes. In offspring rats exposed to maternal PM_2.5, intrarenal expression of renin, angiotensin-converting enzyme (ACE), cytochrome P450 27B1, and vascular endothelial growth factor-A (VEGF-A) increased, while expression of the vitamin D receptor, Klotho, VEGF receptor 2, angiopoietin-1, and Tie-2 decreased. Maternal vitamin D supplementation restored VEGF receptor 2 and angiopoietin-1 activities and reduced ACE and VEGF-A protein expression in adult offspring kidneys. Conclusions: Early-life exposure to PM_2.5 may lead to long-term alterations in renal microvasculature and nephron loss. Maternal vitamin D supplementation during renal development can ameliorate PM_2.5-induced capillary rarefaction and nephron loss in the kidneys of adult offspring. Full article

Airborne particulate matter is a major pollutant globally due to its impact on atmospheric processes and human health. Depending on their aerodynamic size, particles can penetrate the respiratory system, with ultrafine particles (UFPs) reaching the bloodstream and affecting vital organs. This study investigates the particle number size distribution (PNSD), particle number concentration (PNC), and lung-deposited surface area (LDSA) in Bogotá, Cali, and Palmira, Colombia. Measurements were conducted at four sites representing different urban and industrial backgrounds using an Electrical Low-Pressure Impactor (ELPI+). Due to the availability and operation of the device, observations were limited to a few days, so the results of this study are indicative and not generalized for the cities. UFP concentrations were highest in Cali (28,399 cm⁻³), three times higher than in San Cristóbal, Bogotá. Fine particles (FPs) exhibited similar patterns across the three cities, with higher concentrations in San Cristóbal (2421 cm⁻³). Coarse particles (CPs) were most prevalent in Palmira (41.37 cm⁻³), and the highest LDSA values were recorded in Palmira and Cali (>80 µm²/cm³), indicating a higher potential for respiratory deposition. These findings highlight the importance of PNSD in health risk assessment in urban areas, providing valuable insights for future studies and strategies to manage air quality in Colombia. Full article

Fine particulate matter (PM_2.5) has garnered significant scientific and public health concern owing to its capacity for deep penetration into the human respiratory system, presenting significant health risks. Despite the implementation of strict environmental policies in China over the past decade to reduce PM_2.5 levels, long-term public health concerns remain a serious issue. Our study aims to provide a high-quality, seamless daily PM_2.5 dataset for China covering the years 2015 to 2024. A two-step PM_2.5 estimation model is established based on a machine learning algorithm and a spatio-temporal decomposition method. First, we utilize the machine learning algorithm XGBoost (EXtreme Gradient Boosting) to address gaps in the daily MAIAC (Multi-Angle Implementation of Atmospheric Correction) AOD (Aerosol Optical Depth), with R²/RMSE (coefficient of determination/Root Mean Square Error) of 0.67/0.2678 compared to AERONET (Aerosol Robotic Network) AOD. Then, a novel approach by integrating XGBoost with EOF (Empirical Orthogonal Function) decomposition is introduced for PM_2.5 estimation. The integration of EOF allows for the incorporation of entire meteorological field information into the PM_2.5 estimation model, significantly enhancing its accuracy: spatial CV (cross-validation)-R² improved from 0.8340 to 0.8935, and spatial CV-RMSE reduced from 13.8177 to 11.0668. Leveraging the newly produced dataset, we analyze the spatio-temporal variations of PM_2.5 across China with EOF decomposition, particularly noting that PM_2.5 levels in the eastern anthropogenic intensive regions continuously declined from 2015 to 2020, and fluctuated steadily during 2020–2024. This research underscores the critical need for sustained and effective air quality management strategies in China. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM_2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM_2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM_2.5 forecasting accuracy. Full article

Evaluating the impact on roadside environments of NH₃ from vehicle emissions is important for protecting the ecosystem from air pollution by fine particulate matter and nitrogen deposition. This study used passive samplers to measure NH₃ and NO_X at multiple points near a major road to observe the distribution of these gases in the area. The impact of NH₃ emitted from vehicles on a major road on the environmental concentration of NH₃ at different distances from the roadside was found to be similar to that of NO_X and NO₂. The concentration of NH₃ rapidly decreased due to dilution and diffusion within approximately 50 m of the road, and after 100 m the concentration remained almost the same or decreased slowly. Furthermore, CO₂ observations taken in the same period along the roadside and in the background yielded a vehicular emission factor of 4–50 mg/km for NH₃, which is comparable with previous research. This emission factor level contributes 4–11 ppb to the NH₃ concentrations in roadside air through the dilution and diffusion process. A correlation was found between the emission factors of NH₃ and NO_X that was different from the trade-off relationship seen when single-vehicle exhaust is measured. Full article

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM_2.5 and PM₁₀, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided. Full article

Nickel, as a toxic trace element in fine particulate matter (PM_2.5), has detrimental effects on both air quality and human health. Based on measurements from 2020 to 2021 using a single-particle aerosol mass spectrometer (SPAMS), this study investigates the properties of nickel-containing particles (NCPs) in Guangzhou. The composition, sources, and temporal trends of NCPs were evaluated and the impact of the clean ship fuel policy introduced in 2020 was also examined. The key findings include: (1) Nickel particles account for 0.08% number fraction of PM_2.5, which is consistent with previously reported mass fraction in PM_2.5. (2) Three distinct types of NCPs were identified, including Ni-fresh, Ni-aged, and Ni-ash. Each type exhibits unique characteristics in size distribution, wind direction dependence, sources, and temporal variations. Ni-fresh particles originate from shipping emissions in the Huangpu Port area 2 km away and are the major contributors to fine nickel particles in the region. (3) Ni-aged and Ni-ash particles, which carry secondary components, tend to be larger (>500 nm) and are representative of regional or background nickel particles. (4) The implementation of the clean ship fuel policy has effectively reduced the number concentrations of NCPs and is beneficial to regional and local air quality. Full article