Search Results (372)

Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a backpropagation neural network and a random forest model, which achieves high-precision identification of haze, clouds, and clear air using FY-4A AGRI geostationary satellite data, with small misclassification rates and high F1 scores. Through detailed comparison with CALIOP observations, THMA performs well over most regions over Asia, successfully extending the traditional binary classification task of distinguishing only clouds and clear air. Notably, the model provides good classification capability in vertically overlapping areas of broken clouds and haze, with minimal misclassification even over bright surfaces such as deserts and ice/snow. Statistical analysis for the year 2022 shows that the annual average number of haze days is 51.3 in China. This study confirms the significant complementary value of satellite remote sensing and ground-based observations for haze monitoring. Full article

Aerosol–cloud interactions (ACIs) remain a long-standing uncertainty in quantifying cloud microphysical properties, convection, and precipitation. There are fewer investigations into the effects of ACIs on the southwest vortex (a mesoscale circulation with a spatial scale of 300–500 km). Satellite-retrieved MODIS data (2002–2022) reveals a decreasing trend in the June–August (JJA) seasonal mean ice droplet effective radius (DER_Ice) over the Sichuan Basin (SCB) since 2013, corresponding to China’s emission reduction efforts. Concurrently, post-2013 trends exhibit a positive shift in cloud-top height (CTH) and a negative trend in cloud-top pressure (CTP), collectively indicative of intensified convective activity. This contradicts the conventional conclusion that increased anthropogenic emissions reduce droplet effective radius (DER) and intensify convection under constant cloud water content. To address this discrepancy, we simulated the precipitation event caused by the southwest vortex (SWV) during 11–14 August 2020, under distinct initial aerosol loading (clean vs. polluted), using the fully coupled WRF-ACI-Full cloud-resolving model (incorporating sophisticated aerosol parameterizations). Results show that increased aerosols reduce basin-averaged precipitation by 0.54% and updraft speed by 0.37% in the polluted case compared to the clean case, which is negligible. These findings differ from previous studies on ACI-related cloud and precipitation responses. Full article

Shallow-convective clouds (SCCs) play important roles in the Earth’s atmospheric system by affecting radiative balance, large-scale circulation, and transport of pollutants. It is common sense that topography exerts substantial impacts on SCCs. However, the underlying mechanisms are not well understood. Here, we performed large-eddy simulations (LESs) to investigate how three-dimensional (3D) topography affected SCCs. The 3D topography was constructed using two widely used two-dimensional (2D) topographies, a bell-shaped ridge varying in the $x$-direction and a series of sinusoidal ridges varying in the $y$-direction. The bell-shaped ridge was the major ridge. The upper parts and lower parts of the sinusoidal ridges were the minor ridges and minor valleys, respectively. The wavelength of the sinusoidal ridges was systematically varied. LESs were also performed separately using the 2D topographies. In the simulations with 3D topography, the upslope winds were mainly over the minor ridges and the return flows were mainly over the minor valleys, which was different from those in the simulations using 2D topographies. The upslope winds promoted the development of SCCs over the major ridges by producing large thermals and high humidity, similar to in the simulations using 2D topographies. Increasing the wavelength of minor ridges enlarged the region with convergence, and thereby increased the size of SCCs. Our results suggest that it is necessary to consider the 3D topography instead of the more conventional 2D topographies when investigating the topographic impacts on SCCs. Full article

The need for efficient water management is critical today, as this resource faces increasing scarcity due to population growth, pollution, climate change, depletion, and overexploitation of water resources. This further exacerbates the problem of intermittent water supply (IWS), where consumers receive running water for less than 24 h a day, 7 days a week, affecting more than one billion people worldwide. This article presents the development and implementation of a smart water metering and monitoring system (SWMMS) for households affected by IWS. The system comprises IoT devices that record water levels and consumption and supply events in real time; cloud computing services to store and process the readings taken by the IoT devices; and a mobile application that allows users to view the available volume, consult their daily consumption history, and receive alerts for prolonged consumption time, overflows, and low water levels. The system was implemented for 115 days in a home suffering from an IWS, where a lower number of consumption events were recorded during the first 40 days of monitoring due to an initial behavioral response to continuous observation (Hawthorne effect), rather than an improvement in efficiency induced by the system. Full article

Radon (Rn-222) is a major indoor air pollutant with significant health risks. This work presents RadonFAN, a low-cost IoT system deployed in two galleries at the Institute of Physical and Information Technologies (ITEFI-CSIC, Madrid), integrating distributed sensors, microcontrollers, cloud analytics, and automated fan control to maintain radon concentrations below recommended limits. Initially, ventilation relied on a reactive, rule-based mechanism triggered when thresholds were exceeded. To improve preventive control, two end-to-end deep learning models based on regression-to-classification (R2C) and direct classification (DC) are developed. A quantitative analysis of predictive performance and computational efficiency is reported. While the R2C model is hindered by the inherent behavior of the time series, the DC model achieves high classification performance (recall > 0.975) with low computational cost (<4 million parameters, 7 million FLOPs). Modifications to the DC model are studied to identify potential performance bottlenecks and the most relevant components, showing that most limitations arise from feature richness and time series behavior. When evaluated against the existing rule-based ventilation system, the DC model reduces both unsafe radon exposure events and energy consumption, demonstrating its effectiveness for preventive radon mitigation. Full article

Aerosol sprayed from the wheels of vehicles driving on wet roads is a significant source of pollution in the vicinity of roads. If it contains residues of chemical de-icing agents, it can contribute to the faster degradation of objects and structures within its reach. The aim of this research was to determine how the direction of the wind and the intensity of traffic affect the dispersion of the aerosol particles. Using a numerical model of turbulent flow incorporating discrete phase modeling, seven variants of wind direction and two traffic intensities represented by the passing of one or two vehicles were simulated. The results showed that when the wind blew from the location where the particle amount was measured, particle deposition was highly concentrated near the road—peaking at 6.5% of the injected amount at a distance of 5 m—followed by a steep decline to negligible levels at 9 m. Conversely, in the opposite wind direction, deposition was lower (<1%) but exhibited a flat profile, maintaining stable particle concentrations even at the most distant sampling plane (13 m). The passage of two vehicles led to a higher number of particles being detected (reaching up to 8.1%) and induced a vertical dispersion plume reaching up to 13 m above the road surface, compared to a maximum of approximately 7 m observed for a single vehicle. A comparison of the simulated data with long-term in situ experimental measurements confirmed a decrease in aerosol particle deposition with distance from the road. The simulations revealed that the aerosol dispersion is influenced not only by the wind or traffic intensity, but also by specific flow conditions resulting from the terrain configuration. In conclusion, the study shows that while increased traffic intensity mainly extends the vertical reach of the aerosol, wind direction determines its spatial distribution. Since the particle cloud is uneven, measuring devices in a single line perpendicular to the road axis may not accurately capture the highest concentrations. Therefore, to reliably capture aerosol dispersion, it is recommended to also place measuring devices in a direction that is parallel to the road, with a spacing of approximately 9 m. Full article

Rapid urbanization and anthropogenic activities have led to a significant deterioration of air quality, adversely affecting human health and ecosystems. The study of transboundary river basins, where air pollution is exacerbated by political and socio-economic factors, is of particular relevance. This paper presents the results of an analysis of the spatiotemporal distribution of pollutants (Aerosol Index (AI), Methane (CH₄), Carbon Monoxide (CO), Formaldehyde (HCHO), Nitrogen Dioxide (NO₂), Ozone (O₃), Sulfur Dioxide (SO₂)) in the ambient air within the Orontes River basin across Lebanon, Syria, and Turkey for the period 2019–2024. The research is based on satellite monitoring data (Copernicus Sentinel-5P), processed using the Google Earth Engine (GEE) cloud-based platform and GIS technologies (ArcGIS 10.8). The dynamics of population density (LandScan) and the impact of military operations in Syria on air quality were additionally analyzed using media content analysis. The results showed that the highest concentrations of pollutants were recorded in Syria, which is associated with the destruction of infrastructure, military operations, and unregulated emissions. The main sources of pollution were: explosions, fires, and destruction during the conflict (aerosols, CO, NO₂, SO₂); methane (CH₄) leaks from damaged oil and gas facilities; the use of low-quality fuels and waste burning. Atmospheric circulation contributed to the eastward transport of pollutants, minimizing their spread into Lebanon. Population density dynamics are related to changes in concentrations of pollutants (e.g., nitrogen dioxide). The results of the study highlight the need for international cooperation to monitor and reduce air pollution in transboundary regions, especially in the context of armed conflicts. The obtained data can be used to develop measures to improve the environmental situation and protect public health. Full article

This paper proposes a complete solution, implementing a low-cost, energy-independent, network-connected, and scalable environmental air parameter monitoring system. It features a remote sensing module which provides environmental data to a cloud-based server and a software application for real-time and historical data processing, standardized air quality indices computations, and a comprehensive visualization of environmental parameters evolutions. A fully operational prototype was built around a low-cost micro-controller connected to low-cost air parameter sensors and a GSM modem, powered by a stand-alone renewable energy-based power supply. The associated software platform has been developed by using Microsoft Power Platform technologies. The collected data is transmitted from sensors to a remote server via the GSM modem using custom-built JSON structures. From there, data is extracted and forwarded to a database accessible to users through a dedicated application. The overall accuracy of the air quality monitoring system has been thoroughly validated both in controlled indoor environment and against a trusted outdoor air quality reference station. The proposed air parameters monitoring solution paves the way for future research actions, such as the classification of polluted sites or prediction of air parameter variations in the site of interest. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved a mean absolute error (MAE) of 0.075 and a correlation coefficient (R) of 0.841 during the 2024 independent validation, significantly outperforming the original CAMS forecast. The system demonstrated robust and consistent performance across varying land cover types, seasons, and AOD conditions, from clean to highly polluted. Empirical orthogonal function (EOF) analysis confirmed that the product successfully captures physically meaningful spatiotemporal patterns, including transboundary pollution transport, regional emission gradients, and topographic effects. Providing real-time, gap-free, 3-hourly daytime AOD, the proposed model overcomes the limitations of cloud-induced gaps in satellite data and the latency and coarseness of reanalysis products. This enables robust operational monitoring and aerosol research across the Korean Peninsula. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

To overcome the limitations of lengthy laboratory testing cycles and insufficient on-site responsiveness, this study developed an online rapid monitoring device for volatile organic compounds (VOCs) in soil–water–air systems based on photoionization detection (PID) technology. The device integrates modular sensor units, incorporates an electromagnetic valve-controlled multi-medium adaptive switching system, and employs an internal heating module to enhance the volatilization efficiency of VOCs in water and soil samples. An integrated system was developed featuring “front-end intelligent data acquisition–network collaborative transmission–cloud-based warning and analysis”. The effects of different temperatures on the monitoring performance were investigated to verify the reliability of the designed system. A polynomial fitting model between concentration and voltage was established, showing a strong correlation (R² > 0.97), demonstrating its applicability for VOC detection in environmental samples. Field application results indicate that the equipment has operated stably for nearly three years in a mining area of Shandong Province and an industrial park in Anhui Province, accumulating over 600,000 valid data points. These results demonstrate excellent measurement consistency, long-term operational stability, and reliable data acquisition under complex outdoor conditions. The research provides a distributed, low-power, real-time monitoring solution for VOC pollution control in mining and industrial environments. It also offers significant demonstration value for standardizing on-site emergency monitoring technologies in multi-media environments and promoting the development of green mining practices. Full article

Warm-cloud hygroscopic seeding is widely used in precipitation enhancement, but the conditions under which seeding amplifies or suppresses rainfall remain unclear. Here, we use a two-dimensional slab-symmetric spectral bin microphysics model from Tel Aviv University to simulate a warm convective cloud that occurred over Hainan, China, on 11 May 2024, and design three sets of sensitivity experiments in which hygroscopic particles of different characteristic diameters are introduced under a fixed-mass injection constraint. We find that seeding with submicrometer particles (0.1–0.9 µm) systematically suppresses precipitation, with the strongest reduction for 0.1 µm particles. When super-micrometer particles (1–9 µm) are used, the precipitation response transitions from suppression to enhancement as particle size increases, and this transition occurs at about 2 µm. Seeding with ultra-giant particles (>10 µm) generally enhances rainfall and also advances its onset, with the enhancement strengthening up to ~60 µm before weakening for even larger particles. We further show that the transitional particle size at which the seeding effect changes sign decreases with increasing background aerosol loading, from maritime to polluted urban conditions. These results identify an environment-dependent critical particle size that governs the sign and efficiency of hygroscopic seeding in warm convective clouds. Full article

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation. Full article