Search Results (102)

Fine particulate matter (PM_2.5) and ozone (O₃) are the main pollutants affecting the air quality in China, yet their common influencing factors and spatial patterns remain unclear. Focusing on the year 2020, this study adopted the least absolute shrinkage and selection operator algorithm to construct land use regression models with 34 environmental variables for the O₃ concentration at the air quality monitoring stations in the Shenyang Metropolitan Area. For comparison, PM_2.5 models had been developed in our previous work using the same approach. Model performance was satisfactory (cross-validated R² = 0.49–0.81 for O₃; 0.56–0.65 for PM_2.5 in our previous study), confirming the robustness of the approach. The results showed that: (1) Tree cover and grassland exerted synergistic, co-directional mitigation on both pollutants, whereas built-up areas and permanent water bodies were positively associated with their concentrations; (2) Longitude, elevation, and population, as well as atmospheric components such as nitrous dioxide column density and aerosol optical depth, displayed opposite effects on both pollutants, indicating trade-offs; (3) Spatially, PM_2.5 played the dominant role in shaping the pattern of combined pollution, with higher PM_2.5 levels than O₃ in nearly half of the area (46.97%), while O₃-dominant regions were rare (4.27%) and mostly confined to localized zones. This study contributes to a deeper understanding of the synergies and trade-offs driving PM_2.5 and O₃ pollution as well as providing a scientific basis for formulating policies on integrated control measures against combined pollution. Full article

This study systematically investigated water-soluble inorganic ions (WSIIs) and their sources in PM2.5 in mountainous urban areas of Chongqing City. PM2.5 monitoring was conducted throughout 2023, spanning one year. The two districts under discussion are the Liang Jiang New Area (LJ) and He Chuan District (HC). The ion chromatography (Dionex Integrion HPIC) method was utilized to quantify eight ions (Cl⁻, SO₄²⁻, NO₃⁻, Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺). The results obtained were then analyzed in conjunction with the EPA PMF 5.0 source apportionment model. The following key findings are presented: the data demonstrate that there is significant seasonal fluctuation in PM2.5 concentrations. The mean winter concentration (64 ± 27 μg/m³) was found to be 3.25 times higher than the mean summer concentration (19.7 ± 2 μg/m³). These fluctuations were primarily influenced by basin topography and unfavorable meteorological conditions. The proportion of PM2.5 mass attributable to WSII ranges from 31 to 33 percent, with the majority of this mass being attributed to secondary inorganic aerosols (SNA: SO₄²⁻, NO₃⁻, NH₄⁺; accounting for 47–85% WSII). The annual NO₃⁻/SO₄²⁻ ratio (0.69–0.80, <1) indicates that fixed sources (coal/industry) dominate, but a winter ratio >1 suggests increased contributions from mobile sources under low-temperature conditions. The sulfur oxidation rate (SOR: 0.35–0.37) is significantly higher than the nitrogen oxidation rate (NOR: 0.08–0.13), reflecting the efficient conversion of SO₂ through wet, low-temperature pathways. PMF identified six sources, with secondary formation (43.8–44.3%) being the primary contributor to the overall process. In urban LJ, transportation (26.1%) and industry (13.6%) have been found to contribute significantly, while in suburban HC, combustion (15.4%) and dust (8.8%) have been determined to have notable impacts. This study recommends the implementation of synergistic control of SNA precursors (SO₂, NOx, NH₃), the strengthening of transportation and industrial management in LJ, and the enhancement of biomass combustion and dust control in HC. Full article

Climate change is significantly affecting water availability, emphasising the need for sustainable strategies such as wastewater reuse. While this represents a promising alternative resource, insufficiently treated wastewater may pose health risks, particularly through aerosol formation during irrigation, which can facilitate Legionella transmission. This study aimed to evaluate the presence of Legionella across various stages in a wastewater treatment plant (WWTP) that reuses effluent for agricultural purposes. Samples from the influent, four treatment phases, and the final effluent were analysed using both culture-based methods and quantitative PCR (qPCR) for Legionella spp. and L. pneumophila. qPCR detected Legionella spp. in all samples and L. pneumophila in 66% of them. In contrast, the culture-based analysis showed much lower detection levels, with only one positive sample at the influent stage—likely due to microbial interference or growth inhibition. Although contamination decreased in the final effluent, Legionella was still detected in water designated for reuse (Legionella spp. in 100% and L. pneumophila in 17% of samples). No treatment stage appeared to promote Legionella proliferation, likely due to WWTP characteristics, in addition to wastewater temperature and COD. These findings underscore the importance of monitoring Legionella in reclaimed water and developing effective control strategies to ensure the safe reuse of treated wastewater in agriculture. Full article

Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article

This study investigates the emission characteristics and environmental impacts of pollutants from bagasse-fired biomass boilers through the integrated field monitoring of two sugarcane processing plants in Guangxi, China. Comprehensive analyses of flue gas components, including PM_2.5, NOx, CO, heavy metals, VOCs, HCl, and HF, revealed distinct physicochemical and emission profiles. Bagasse exhibited lower C, H, and S content but higher moisture (47~53%) and O (24~30%) levels compared to coal, reducing the calorific values (8.93~11.89 MJ/kg). Particulate matter removal efficiency exceeded 98% (water film dust collector) and 95% (bag filter), while NOx removal varied (10~56%) due to water solubility differences. Heavy metals (Cu, Cr, Ni, Pb) in fuel migrated to fly ash and flue gas, with Hg and Mn showing notable volatility. VOC speciation identified oxygenated compounds (OVOCs, 87%) as dominant in small boilers, while aromatics (60%) and alkenes (34%) prevailed in larger systems. Ozone formation potential (OFP: 3.34~4.39 mg/m³) and secondary organic aerosol formation potential (SOAFP: 0.33~1.9 mg/m³) highlighted aromatic hydrocarbons (e.g., benzene, xylene) as critical contributors to secondary pollution. Despite compliance with current emission standards (e.g., PM < 20 mg/m³), elevated CO (>1000 mg/m³) in large boilers indicated incomplete combustion. This work underscores the necessity of tailored control strategies for OVOCs, aromatics, and heavy metals, advocating for stricter fuel quality and clear emission standards to align biomass energy utilization with environmental sustainability goals. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

The long-term monitoring of nighttime lights is essential for understanding sources of light pollution. Nighttime lights observed in space are affected by atmospheric conditions as they transmit from the Earth surface through clouds and aerosols to the top of the atmosphere. In this study, based on the monthly cloud-free VIIRS/DNB products, we analyzed the long-term nighttime lights in Hong Kong (2012–2020). We found that the monthly variations in nighttime lights were large, especially in bright regions. The 12-month average of nighttime lights ranged from 13.0 to 18.9 nWcm⁻²sr⁻¹. Public transportation facilities, such as port facilities and the airport, were the brightest, twice as bright as other urban areas. Public residential areas were slightly brighter than private ones. These urban areas were at least four times brighter than undeveloped regions, showing a significant alteration in light at night due to artificial facilities. Further, we used an unsupervised clustering method to identify specific patterns. While nighttime lights were stable in most regions, increasing trends were found at construction sites of a new artificial island and the airport expansion. Abnormal patterns, such as wildfires, were also recognized. We found that the background nighttime lights were brighter in wet months (e.g., April) and dimmer in dry months (e.g., January). The amount of water in the atmosphere affects nighttime light scattering, with a linear correlation (R = 0.68) between humidity and the occurrence of bright nighttime lights each month. The diverse sources and variations in nighttime lights call for continuous monitoring and advanced analytical methods to better understand their environmental and societal impacts. Full article

Legionella bacterium has the aquatic environment as its natural reservoir. In humans, it can cause a form of interstitial pneumonia called legionellosis which can be transmitted by inhalation of contaminated water aerosols. Legionella infection occurs more frequently in certain more susceptible population groups, including smokers, alcoholics, men, the elderly, as well as people with acquired immunodeficiency syndrome, hematological cancers, and diabetes mellitus. This study aimed to evaluate the effectiveness of the new Italian National Guidelines for the prevention of Legionella colonization in water systems application by analyzing the environmental monitoring data of Legionella carried out in healthcare facilities in the Campania region from 2019 to 2022. The secondary objectives were to estimate the most observed serogroups of L. pneumophila and to analyze the possible link between water temperature and the presence of Legionella, respectively. From our data, it emerged that in 2019, 41.1% of the examined facilities were contaminated by the Legionella genus; in 2020, the contamination percentage was 42.9%; in 2021, it was 54.5%; in 2022, it was 45.5%. Instead, the Legionella positivity rate decreased from 2019 (54.3%) to 2022 (52.4%), suggesting a possible positive influence of more restrictive prevention and control measures. The prevalent species was Legionella pneumophila, particularly serogroup 1; water temperature was the risk factor implicated in Legionella contamination. Full article

Ultraviolet (UV) radiation effects on Earth’s ecosystems on a global scale can be assessed on a basis of satellite estimates of hyperspectral irradiance on the surface and in ocean waters and the spectral biological weighting functions. The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. The assessment of in-water irradiance requires additional information on inherent optical properties (IOPs) of ocean water. Our Ozone Monitoring Instrument (OMI) surface hyperspectral irradiance algorithm is updated by implementing a new absorbing aerosol correction based on OMI daily retrievals of UV aerosol absorption optical depth (AAOD). To provide insight into the temporal and spatial variability of absorbing aerosols, we consider a monthly global AAOD climatology derived from the OMI UV aerosol algorithm. Hyperspectral underwater irradiance is computed using Hydrolight radiative transfer calculations along with a Case I water model of IOPs extended into UV. Both planar and scalar irradiances are computed on the Earth’s surface and propagated underwater. The output surface products include the UV index. The output underwater products include the hyperspectral diffuse attenuation coefficients of the planar and scalar irradiances. Effects of the seasonal variability of AAOD on the UV index and the deoxyribonucleic acid (DNA) damage dose rates are considered. The reduction in the UV index and DNA damage dose rate due to the presence of absorbing aerosols can be as large as 30–40%. Full article

Particulate matter (PM) is widely recognized as a major air pollutant with significant impacts on human health, highlighting the need for accurate monitoring. In developing countries, low-cost sensors are crucial for accessible PM monitoring, but their accuracy and reliability must first be assessed. This study benchmarked the Alphasense OPC-N3 and Next PM sensors through laboratory and field evaluations. Laboratory tests were performed in controlled conditions with HEPA-filtered air at low humidity and varying concentrations of water droplets from nebulized deionized water. A 27-day field study in Cienfuegos, Cuba, provided additional insights into real-world performance. The OPC-N3 showed susceptibility to perturbations and was more affected by outliers (especially PM₁₀), relative humidity, and interference from aqueous aerosols. In contrast, the Next PM sensor demonstrated superior stability, lower noise levels, and consistent performance across different environmental conditions. Despite a substantial price difference, both sensors provided valid measurements. Additionally, both sensors produced lognormal PM concentration distributions during field campaigns. This feature could aid in addressing the calibration stability challenges commonly associated with low-cost sensors through in situ calibration methods. While the PM measurements by affordable sensors are not perfect, they are sufficiently reliable for supporting air quality assessments in resource-limited settings. Full article

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance ($R_{rs}$) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides $R_{rs}$ products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution $R_{rs}$ products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal $R_{rs}$ products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates $R_{rs}$ values at shorter wavelengths, particularly at 412 nm. While the $R_{rs}$ accuracy at 412 nm is relatively low, G-Portal’s $R_{rs}$ products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower $R_{rs}$ magnitudes and Chla concentrations below 5 mg/m³. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in $R_{rs}$ data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments. Full article

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM²) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events. Full article

Water-soluble inorganic ions (WSIIs) can increase the hygroscopicity of aerosols, which will transform aerosols into larger sizes and reduce visibility by enhancing light scattering. To explore the characteristics of WSII concentrations and their impacts on visibility in a coastal airport, in this study, PM_1.0 samples at two monitoring sites (including airport site and background site) were collect in spring and summer, and 12 species of ions were detected. In general, secondary water-soluble inorganic ions (SNA, including ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$ and ${\mathrm{NH}}_4^{+}$) and ${\mathrm{Ca}}^{2+}$ were the dominant WSIIs in PM_1.0, contributing about 89% to 95% of the total measured ions. The continental contributions of ${\mathrm{SO}}_4^{2-}$, ${\mathrm{K}}^{+}$, and ${\mathrm{Ca}}^{2+}$ accounted for more than 60% during the whole period, while Na⁺ and ${\mathrm{Cl}}^{-}$ were mainly from marine sources. The source identification showed that airport emissions were a major source at the sampling site and significantly contributed to the levels of sulfate, nitrate, and ammonium. Agricultural activities were the dominant sources impacting visibility in spring, while airport emissions and secondary inorganic aerosols were the main components affecting visibility in summer. Therefore, improving atmospheric visibility in coastal airport areas should focus on reducing the precursors of secondary particulates and reducing biomass-burning activities. Full article

Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. Full article

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. Full article