Surface Temperature Monitoring Research Articles

Pervious asphalt pavement- and concrete wall-watering for urban cooling: Characterization of surface cooling effects at an outdoor test site

Pervious pavement- and wall-watering was studied at a 100 m2 pilot site in Hy'eres (France). Surface temperature monitoring was conducted continuously with an infrared camera to assess watering's cooling effects and duration as well surface drying time for the concrete fa¸cade and the pervious pavement. Watering was conducted every hour for 15 min from 8 am to 6 pm in summer 2021. Results show up to 14.3 °C surface temperature reductions for pervious pavementwatering, and up to 8.4 °C of cooling for wall-watering applied to a South-facing wall. For shaded surfaces, maximum cooling is limited to 5 °C, whether vertical or horizontal. Between watering cycles, the vertical and impervious wall surface dries in approximately 15 to 20 min. For the pervious pavement, drying time varies along the surface. While certain areas, downstream, don't dry between watering cycles, others dry in 25 to 35 min. These variations depend on the relative height of the considered zone along the pavement slope, as well as the pavement's material and topographical irregularities. Residual cooling effects are observed the following night until the next morning for both pavement- and wall-watering, in the order of 3.5 °C and 1 °C at 6 am, respectively. Results further illustrate the higher efficiency of urban watering solutions for surfaces in direct sunlight.

Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries

Abstract Land surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many applications, by the harmonic analysis of time series algorithm (HANTS) and to investigate the possibility of improving LST reconstruction accuracy using Landsat 8 and 9 images simultaneously. For these aims, 91 Landsat 8 and 9 images with 100 m spatial resolution in 2022 and 2023 are employed, covering Yazd-Ardakan plain in Iran. Three methods are used for evaluation. In method one, a part of LST image is considered as a gap and is compared with the initial value after reconstruction. In method two, on a cloudy day and a cloudless day, surface temperature values are measured using thermometers at fifty points in plain lands, and the difference between gap-filled satellite measurements and ground measurements is calculated. In method three, all the reconstructed LST images are compared with the original images. In method one, the root mean square error (RMSE) of reconstructed LST reduces by 1.3 °C when using the combined Landsat 8 and 9 images. In method two, RMSEs of reconstructed LST images are 6.1 °C when using Landsat 8 and 5.4 °C when using the combined Landsat 8 and 9. Method three shows that 41% of the study region has RMSE of less than 2 °C when using only Landsat 8, while this value becomes 72% when combining Landsat 8 and 9. In general, the combined use of Landsat 8 and 9 LST images improves the accuracy of reconstruction using HANTS. The findings of this research are crucial for regional applications and remote monitoring of surface temperature in areas with limited weather stations.

High-quality temperature imaging of Li2TiO3: Mn4+ thin film based on time-resolved luminescence measurement

With the rapid progress and mass production of integrated electronic devices in micro and nanometer scale, it is urgent to develop a technology that can monitor surface temperature distribution of integrated device for the purpose of optimizing the device design. Herein, a thermometry based on time-resolved measurements of luminescence from Li2TiO3: Mn4+ was presented, which uses the ratio of integrated fluorescence intensities in two different time windows. The scheme significantly improves the relative sensitivity of temperature sensing, with maximum value reaching 5.81 % K−1 at 342 K. With the help of a fluorescence microscope and an intensified charge coupled device (ICCD), the scheme based on the time-resolved measurement of Li2TiO3: Mn4+ was successfully applied to the temperature imaging of nickel circuit surface. By comparing the performance of temperature imaging via powder phosphor with that via thin film, it can be found that the thin film can better reflect the true temperature distribution on the surface of nickel circuit. What’s more, the Li2TiO3: Mn4+ thin film also improves the resolution of the temperature imaging (δT=0.61 K). Therefore, it is significant to explore the temperature measurement scheme based on the time-resolved measurement of Li2TiO3: Mn4+ thin film for realizing high-quality temperature imaging.

İskenderun Körfezi kıyı alanlarında sıcaklık ve klorofil-a için uydu ve model temelli veri setlerinin temsil yeteneği üzerine bir değerlendirme

This study investigates the monitoring of sea surface temperature (SST) and chlorophyll-a (chl-a) levels in Iskenderun Bay using satellite and modeling data and evaluates the possible use of these datasets for monitoring marine ecosystems. Datasets derived from MODIS-Aqua satellite imagery and modeling data obtained from the Copernicus MyOcean and in-situ measurements were used in the study. According to the analysis on paried data sets of the distribution of SST and chl-a, sattelite and model datasets showed statistically significant correlations with in-situ measurements for SST. However, only satellite dataset showed significant correlations for Chl-a. Evaluations on uncertainty of the data sets revealed that the satellite dataset had a narrower range and less outlier distribution for SST. For chlorophyll-a, both datasets had wide uncertainty ranges and required further improvement. This study highlights the potential of satellite and model datasets for monitoring SST and chl-a variations in Iskenderun Bay.

DeepTimeNet: A novel architecture for precise surface temperature estimation of lithium-ion batteries across diverse ambient conditions

With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet’s performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet’s superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.

Irregular surface temperature monitoring in liver procurement via time-vertex signal processing

The liver viability monitoring during its procurement is critical to guarantee the safe liver transportation. Traditionally, the viability is assessed by taking invasive biopsy on the liver surface. Recently, the noninvasive thermal images of liver surface have been used as an alternative assessment way. Researchers have proposed monitoring and classification approaches based on the thermal images. Existing works have demonstrated the importance of the temporal variation or the spatial variation of the thermal images to the viability monitoring. However, there is no prior work to leverage the graph structure of the spatio-temporal processes for the viability monitoring. In this paper, we propose a time-vertex signal processing framework for the irregular thermal data of the pure liver region monitoring. In particular, we extract features of irregular thermal data based on the joint Fourier transform (JFT), which is an integration of graph Fourier transform (GFT) and discrete Fourier transform (DFT). The extracted JFT features can accurately reconstruct the thermal images with a limited number of features. Then, we use a non-parametric online change-point estimation method, based on online scan B statistics, to monitor the JFT features without a parametric distribution. Our proposed framework is applied to both simulation and the liver procurement data, and achieves good monitoring performance.

Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas

Abstract Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi‐layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non‐linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health.

Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques

ABSTRACT Advancements in satellite technology coupled with image processing, statistical tools and Artificial Intelligence (AI) algorithms, enable efficient, non-invasive, real-time monitoring and analysis of landfills. Landfills pose a severe environmental threat to the local ecosystem. Metropolitan cities in most developing countries usually have multiple landfills. Landfill Land surface temperature (LST) monitoring can help keep track of the changes in landfills, especially concerning waste management, land use, hotspot movement, fire prediction and the stability of the landfill. Monitoring these waste disposal sites through remote sensing data can provide real-time monitoring and analysis, supporting quick interventions when necessary. This research utilizes LANDSAT 8 satellite images for Mumbai, India. Using geospatial analysis, image processing and data processing, LST is derived for 576 LST data points. While landfill LST has a positive trend over time, but there are cyclical, seasonal and changes due to disruptive events. With landfill LST consistently remaining the highest over the years, in comparison to built-up, green and sea LSTs. A statistical analysis of the data reveals the overall significance of the model in ascertaining the increase in temperature due to the presence of landfills. Despite challenges such as large data libraries and variable environmental factors, this research emphasizes and contributes to satellite imagery’s evolving potential for monitoring and assessing LSTs.

Early signals of El Niño 2023 in the Gulf of California?

After a long La Niña event (2020-2023), the US National Oceanographic and Atmospheric Administration warned of a quick transition to an El Niño phase. As a monitoring of sea surface temperature (SST) and chlorophyll-a (Chl-a) levels in the Gulf of California, Mexico, this study aims to show the evolution of both parameters during the first seven months of 2023. It compares them with the first seven months of 2015 when an extreme El Niño event occurred. Satellite data from the Moderate Resolution Imaging Spectroradiometer were obtained from which maps of both variables were constructed to quantitatively assess the changes in the SST and the Chl-a levels during the selected periods. Satellite data/pixels were extracted from the southern region of the gulf, in the connection between the gulf and the open Pacific Ocean. The results showed a gradual increase in the SST values from February to July of 2023 with 20.43, 22.29, 22.77, 27.88, 28.72 and 31.64ºC, respectively (a thermal variation of 11.21ºC), while Chl-a levels showed a decrease over the same period, from 1.81, 1.10, 0.78, 0.24, 0.19 mg m-3 to unusually low values of 0.11 mg m-3, respectively (a reduction of 1.70 mg m-3). A similar pattern was found in 2015, with increased SST and decreased Chl-a. Were the observed patterns in 2023 the early signals of El Niño in the Gulf of California?

Elevated water transport of polyamide nanofiltration membranes via aqueous organophosphorus mediated interfacial polymerization

As interfacial polymerization (IP) is a reaction-diffusion process, rational regulation of diamine diffusion rate is effective to heighten the nanofiltration (NF) performance of polyamide (PA) membranes. Herein, we propose an aqueous organophosphorus-regulated IP strategy that promotes the formation of thinner and moderately crosslinked films toward improved water transport. Two similar compounds tetrakis (hydroxymethyl) phosphonium chloride (THPC) and tetrakis (hydroxymethyl) phosphonium sulfate (THPS) were utilized as functional additives to physically interact with piperazine via hydrogen bonding and thus retard piperazine diffusion rate during an IP process. The resultant polyamide membranes have been analyzed in terms of surface morphologies, chemical structures, hydrophilicity, and separation performance in detail. Surface temperature monitoring results revealed that heat release from regulated IP was less than that from unmodified IP, which was ascribed to retard monomer diffusion toward organic boundary in the presence of THPC or THPS. Molecular dynamics simulation further confirmed that the addition of THPC and THPS could efficiently decline the monomer diffusion rate via hydrogen bonds and increased solution viscosity. The modified PA membranes exhibited a reduced film thickness, moderate crosslinking degree, as well as increased average pore size, as revealed by a series of characterizations. Furthermore, the incorporation of THPC or THPS resulted the PA membranes with higher surface hydrophilicity and stronger negative charges. These improved physicochemical features synergically conferred a two-fold water permeance while maintaining a high divalent salt rejection (THPC-0.01: 18.3 L m-2 h−1 bar−1, 99.0 %, THPS-0.015: 20.2 L m−2 h−1 bar−1, 98 %). This aqueous organophosphorus regulated IP offers a simple and feasible approach to the preparation of low-transport-resistance PA membranes for enhanced NF performance.

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

An Improved Analytical Thermal Rating Method for Cable Joints

To improve the utilization rate of cable lines while retaining sufficient security, the accurate thermal assessment of cable is significant for cable operation condition evaluation. The thermal rating for a cable joint, which is regarded as a hot spot of cable lines, is not covered by the scope of IEC 60287. While the existing publications for cable joint thermal evaluation also have some limitations. In this paper, the quasi-three-dimensional thermal model of the cable joint was established and the iterative solution method for the model is presented. Based on the model, an improved thermal rating method for the cable joint was proposed, which was implemented with monitored surface temperature and load data. The improved method was verified by the finite element method and the results showed an error of less than 5%. The superiority of the improved method was conducted by the comparison between the previously published method and the improved method. The improved method showed a better accuracy than the previously published method. The proposed method in this paper can be complementary to the IEC method, and is easy to use for the operating evaluation of cable joints in the field with the on-line condition monitoring technology.

Plant Tattoo Sensor Array for Leaf Relative Water Content, Surface Temperature, and Bioelectric Potential Monitoring

AbstractThis study presents a wearable plant tattoo sensor array designed for continuous monitoring of leaf temperature, relative water content, and biopotential. Current plant wearable sensor technologies often require relatively bulky substrates for sensor support and adhesives for leaf attachment, which potentially can hinder plant growth and affect long‐term measurements. The multifunctional tattoo sensor array overcomes these issues by adhering directly to the leaf surface without the need for additional supporting structures or glues. This array includes a biopotential electrode, a resistive temperature sensor, and an impedimetric water content sensor, all constructed using laminated gold‐on‐polymer thin‐film patterns. Due to their mechanical flexibility, stretchability, and conformability, the sensors can seamlessly attach to leaves via van der Waals force. Performances of these sensors are evaluated to explore plant responses under diverse growth environments. This sensor array is capable of both short‐term and long‐term monitoring, offering continuous data and detailed insights into plant physiological responses to various stress conditions.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Self-powered wireless flexible sensing for food storage based on triboelectric-electromagnetic generator

In Controlled Atmosphere (CA) food storage, maintaining optimal conditions, including gas concentration, temperature, and humidity on the food surface, is crucial. This paper proposes the application of a self-powered wireless flexible sensing system based on a triboelectric-electromagnetic generator in food CA storage. To configure certain concentrations of mixed CA gases, we need to accurately control the volume of CA gas input into the mixed gas tank within a certain period, and the gas flow velocity in the pipeline will directly affect this factor. The gas flow velocity in the pipeline is detected by the CA gas flow velocity detection part (FVS) with an independent layer working mode triboelectric nanogenerator (FV-TENG) as the main component. Simultaneously, a flexible Temperature and Humidity Sensor (THS) affixed to the food surface provides real-time monitoring. EMG collects irregular wind energy in the CA warehouse to provide energy for the processing of information in the system and wireless transmission to the IoT cloud platform, realizing the system's self-power and wireless transmission. The self-powered wireless sensing system facilitates remote collection of CA gas flow velocity data, precise control of mixed atmosphere gas concentrations, and real-time monitoring of food surface temperature and humidity in the CA warehouse.

Validating Landsat Analysis Ready Data for Nearshore Sea Surface Temperature Monitoring in the Northeast Pacific

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R2 of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (>10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST.

Diurnal to Decadal Variability in Land Surface and Air Temperature Gradient from 2002 to 2022 over the Contiguous United States

Abstract The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.

Wear and Friction Behaviour of Additive Manufactured PEEK under Non-conformal Contact

Tribological properties of laser sintered polyether-ether-ketone (EOS PEEK HP3) were investigated using a rolling-sliding test rig. This investigation aimed to study the wear and friction failure mechanism of EOS PEEK HP3. The main objectives included to conduct wear and friction tests under non-conformal contact, to monitor surface temperature, to carry out surface characterization with microscopy. With this rolling-sliding test rig, tests were carried out on an EOS PEEK HP3 specimen running against a steel disc unlubricated, with various slip-ratios under a contact pressure of 56 MPa, 48 MPa and 39 MPa respectively. Both wear and friction were measured. The results have shown that both friction and wear were increased with an increase of either slip-ratios or the contact pressures, exacerbated by high surface temperatures. It has also been observed that both friction and wear failures were associated with the degradation of the non-conformal contact surfaces due to crystallinity changes that correlated well with working conditions. Using microscopy it was found that such failures as pitting, fatigue and surface cracking were affected by the surfaces in contact, including the degree of melting of the surface. Based on the observation on the contact surfaces, the failure mechanisms of EOS PEEK HP3 include surface melting and contact fatigue failures with the high slip-ratio and the high contact pressure conditions. The findings of this investigation have the potential to help to design & develop additive manufacturing PEEK products. Typically, these results can be used in a design process for a more effective polymeric gear system.

High-resolution induction thermography for detection of micro-cracks in Ti-6Al-4V alloys

ABSTRACT This study focuses on establishing a non-contact Non-Destructive Testing (NDT) method for detecting micro-cracks in Ti-6Al-4 V sample using the Eddy Current Thermography (ECT) technique. The study employs an induction coil for heating the sample and a thermal camera equipped with magnifying lenses to monitor surface temperature distribution. The variations in the thermal image signify the presence of defects. The efficacy of the proposed method is demonstrated through the examination of four samples featuring artificially created short notches, representing micro-cracks. These notches vary in dimensions, with notch openings approximately 20 µm, notch depths ranging from 1–10 µm, and notch lengths varying from 5 to 150 µm. The investigation explores the influence of the length and depth of short cracks on defect detection, revealing an increase in thermal contrast with both the length and width of the cracks. All notches are successfully detected using the proposed method, showcasing its applicability for real crack detection. The study concludes with the successful application of the method on a sample with actual cracks measuring less than 10 µm in width, 2 µm in depth, and a length of 100 µm.

Air quality impacts of landfill fires: A case study from the Brahmapuram Municipal Solid Waste Treatment Plant in Kochi, India

The occurrence of waste fires in unscientifically managed landfill sites has become a pressing environmental issue in the urban centers of developing economies. In the present work, an investigation was carried out to evaluate the air quality implications of three major fire events that occurred at the Brahmapuram Municipal Solid Waste Treatment Plant (BMSWTP) in Kochi, India. Initially, Landsat-based surface temperature monitoring was conducted to identify the thermal hotspots within the landfill. The emissions of different pollutants during waste fires were quantified and compared between satellite-based ex-situ and field-based in-situ methods. The dispersion patterns of PM2.5 particles released during the fires were visualised using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) particle dispersion model. The Landfill Gas Emissions Model (LandGEM) was employed to quantify the greenhouse gases (GHGs) released during waste storage, which was then compared with the GHGs emissions during waste fires. In-situ emission estimates showed that the combustion of waste at BMSWTP led to the release of 909.3 MT of PM10, 938.8 MT of PM2.5, 5832.9 MT of CO, 43.6 MT of SOx, 284.2 MT of NOx, 138,941.9 MT of CO2, 426.8 MT of CH4, and 2665.1 MT of VOC. However, a noticeable disparity was observed between the in-situ and ex-situ emission estimates, wherein the latter underestimated the actual emissions. Most of the emitted PM2.5 particles propagated oceanward under the influence of prevailing winds, covering the densely populated areas of Kochi municipal corporation. The amount of CH4 and CO2 emitted during the waste fires was on par with the emissions from 159 days of waste storage for CH4 and 51.8 years of waste storage for CO2, with a cumulative global warming potential of 147.9 Gg CO2-e.