Ambient Temperature Data Research Articles

Energy and exergy analysis of a 20-MW grid-connected PV plant operating under harsh climatic conditions

Abstract Large grid-connected photovoltaic (PV) plants are increasingly being installed around the world, including in harsh desert climates. Evaluating their performance can help improve the design and operation of PV systems. This study performed an energy and exergy analysis of a 20-MW grid-connected PV plant under desert climatic conditions in southern Algeria over a period of 1 year. The PV plant was divided into two 10-MW subsystems. Energy analysis was performed using actual irradiation, power output, wind speed and ambient temperature data. The annual average energy efficiency of the plant and subsystems was 10.82%, 10.95% and 10.69%, respectively. Solar radiation had the most significant impact (80% determination coefficient) on thermal exergy loss. The exergy efficiency of the plant was lower than the literature values, likely due to the harsh desert conditions. The comprehensive energy and exergy analysis provides insights into the performance of large-scale PV plants in desert climates. The results can help guide the system design and operation improvements for such conditions. Regular cleaning and cooling could improve performance.

Canopy protection cyber-physical system (CPCPS) for smart agricultural management of frost damage in apple orchards

Frost events have resulted in huge economic losses in agriculture. Apple flower buds are susceptible to being damaged by cold weather in early spring, leading to reduced apple production. Applying heat is one of the most effective methods to prevent apple flower bud damage. However, growers generally determine when and how to apply heat in orchards based on their experiences and available labor force, leading to either insufficient heating or energy waste. In this study, a frost protection cyber-physical system (CPCPS) was developed to make heating decisions based on real-time ambient temperature and wind direction data. The CPCPS consisted of a real-time temperature sensing system, an angle auto-adjusted heater, and an unmanned ground vehicle (UGV) to move the heating system. The results show that the CPCPS improved protection performance during the field tests. Compared to the non-CPCPS, the CPCPS increased the percentages of canopy protected time by 198.8 % and 111.9 % in two low-temperature field tests. The outcome of the study can be beneficial for enhancing protection performance of canopies in apple orchards.

Soil Moisture Prediction Model Based on Improved GRU Recurrent Neural Network

Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.

Seasonal effect on phenolic content and antioxidant activity of young, mature and senescent leaves from Anredera cordifolia (Ten.) Steenis (Basellaceae).

Anredera cordifolia (Ten.) Steenis is a vine species native to Brazil that is considered an unconventional food plant and a medicinal species whose phenolic compounds exert antioxidant activity. Since the production of metabolites is determined by environmental factors and leaf maturity, it is important to track these changes in order to determine the best time to harvest. This study aimed to verify whether leaf phenology and seasonality cause variations in the amount of phenolic compounds and in the antioxidant activity of this species. The leaves were collected in different seasons between September 2018 and April 2019, and separated according to maturity: young, mature, and senescent. Daily atmospheric temperature and rainfall data were used to characterize the collection period. The total phenolic content (TPC), determined by Folin-Ciocalteu method, was significantly higher in the young leaves collected in winter, a season of lower temperatures. These leaves showed 54.4 mg of gallic acid equivalents per 100 g of dry matter (mg GAE 100 g-1DM). Other results averaged 25.6 mg GAE 100 g-1DM. The highest antioxidant activity, assessed via the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method, regardless of leaf phenology, was observed in leaves harvested in autumn (73.7%) and winter (71.1%), seasons with lower rainfall. Leaves harvested in summer and spring had lower antioxidant action rates (54.3 e 37.5%, respectively). There was no significant correlation between the total phenolic content and antioxidant activity. Thus, the phenolic composition of A. cordifolia, and consequently its activity on free radicals, varies seasonally in response to temperature and rainfall, and may or may not interact with the age of the leaves.

Comparing Atmospheric Temperature Fluctuations Across Landed Missions

AbstractWe analyze and compare atmospheric temperature data from three landed missions: Mars Science Laboratory (MSL) Curiosity rover, Phoenix lander, and Pathfinder lander. Pathfinder and Phoenix were lander missions that operated for 84 and 151 sols, respectively. MSL Curiosity is a rover that operates on the surface of Mars. It has recorded air temperature for more than five Mars Years (MY). We denoise and detrend temperature data from each mission and use those results to calculate variance in air temperature as a diagnostic for atmospheric variability at the surface. The results show a consistent seasonal pattern in MSL air temperature variance with little interannual variability outside major dust storms. The global dust storm in MY 34 was accompanied by a decrease in temperature variance and a muted response in peak MY 35 variance the following year. Phoenix (68°N, 2 m measurement height) and Pathfinder (19.7°N, 1.1 m measurement height) air temperatures have larger variance than air temperature from environmental data records at the MSL location (5.4°S, 1.6 m measurement height) at its equatorial latitude. Pathfinder variances per sol are larger than those of Phoenix, possibly due to a combination of Pathfinder's lower albedo surface and lower latitude. This occurs despite the Pathfinder location's higher thermal inertia, which would act to decrease noontime variance relative to a lower thermal inertia surface. Comparison of MSL temperature variance to pressure drops related to convective vortex activity shows consistent seasonal patterns; however, pressure drops tend to increase with increasing rover elevation, while variance remains consistent.

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

Unravelling the elevation-dependent warming in the Indus Basin

The Elevation Dependent Warming (EDW) in the mountain regions have wider implications to its cryosphere, hydrological, and ecological systems. This paper explores an EDW in the Indus River Basin, considering Jhelum, Kabul, and Indus sub-basins to improve the accuracy and understanding of cryospheric and hydrological changes in the basin. We use the maximum (Tmax), minimum (Tmin), and mean (Tmean) near surface atmospheric temperature data from 21 ground stations for the 1988–2021 period and applied the Mann-Kendall (MK) trend tests for exploring the trends in the data. The results show that Tmax and Tmin has significant (p < 0.001) warming trends in the Upper Indus Basin (UIB) until 2210 mean above sea level (m asl). Tmax has increased with a rate of 0.031 °C yr−1 (p < 0.001) while Tmin increased with a rate of 0.029 °C yr−1 (p < 0.001), hence the “diurnal temperature range (DTR) also increased significantly” (0.015 °C yr−1, p < 0.001). In this study, a symmetric pattern of increasing trends has been observed in Tmax and Tmin. Moreover, Tmax has increasing trends throughout the period in the middle and high-altitude regions, except in Autumn and Monsoon seasons, respectively. While more significant and prominent increasing trend observed in Tmax during the winter season. Thus, this consistent warming trends in all seasons with more prominent trend in the winter season for Tmax can have harmful impact on the cryosphere of the region and ultimately, leading to water scarcity and extreme droughts.

Modeling, optimizing and sizing of a solar air conditioning system with refrigerant storage for meeting the 24-hour cooling load of a restaurant building

A methodology is presented for the sizing of a solar integrated absorption refrigeration system considering the dynamic cooling load, solar energy and ambient conditions. The solar vapor absorption system (single effect, LiBr) is designed to meet the cooling demand of a 200 m2 restaurant building. Using the genetic algorithm, the operating variables of the absorption refrigeration system are optimized The system is to be integrated to a solar collector to meet the varying cooling load profile. The local solar insolation profile at a location is considered in the analysis. The ambient temperature data is utilized to model how the system works in real time. For continuous operation, to meet the 24 h load profile, refrigerant storage is considered for the system. The amount of refrigerant stored is 1529 kg. The optimal area required for the solar collector is found to be 308 m2. The hot water storage tank volume is 5.2 m3. The selection of working fluid (LiBr-water), solar collector (flat plate), and design (single effect system) guarantees that the system is effective, economical, and environmentally favorable.

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

Ambient temperature and kidney function in primary care patients

IntroductionExposure to high ambient temperatures is associated with a risk of acute kidney injury. However, evidence comes from emergency departments or extreme weather exposures. It is unclear whether temperature-related adverse kidney outcomes can also be detected at a community level in a temperate climate zone.MethodsIn a 9.5-year retrospective cohort study we correlated estimated glomerular filtration rate (eGFR) values of Swiss adult primary care patients from the FIRE cohort (Family medicine Research using Electronic medical records) with same-day maximum local ambient temperature data. We investigated 5 temperature groups (< 15 °C, 15–19 °C, 20–24 °C, 25–29 °C and ≥ 30 °C) as well as possible interactions for patients with increased kidney vulnerability (chronic heart failure, diabetes, chronic kidney disease, therapy with renin–angiotensin–aldosterone-system (RAAS) inhibitors, diuretics or non-steroidal anti-inflammatory drugs).ResultsWe included 18,000 primary care patients who altogether provided 132,176 creatinine measurements. In the unadjusted analysis, higher ambient temperatures were associated with lower eGFR across all age and vulnerability groups. In the adjusted models, we did not find a consistent association.The highest ambient temperature differences (> 25 or > 30 versus < 15 °C) were associated with marginally reduced kidney function only in patients with ≥ 3 risk factors for kidney vulnerability, with a maximum estimated glomerular filtration rate reduction of −2.9 ml/min/1.73m2 (SE 1.0), P 0.003.DiscussionIn a large primary care cohort from a temperate climate zone, we did not find an association between ambient temperatures and kidney function. A marginal inverse association in highly vulnerable patients is of unclear clinical relevance.Graphical abstract

Qualitative Analysis of the Cooling Potential of the Streetside Vegetation of Kharagpur, West Bengal, India

The urban heat island (UHI) effect is a major challenge in contemporary urban planning. The large number of paved surfaces, anthropogenic activities and combustion of fossil fuels are the major contributors of this phenomenon. Urban green spaces are known to mitigate the UHI. Since streets constitute a major portion of the urban areas, greening the streets can help reduce heat stress significantly. To study the impact of orientation and density of the street side vegetation on their cooling potential in Kharagpur, West Bengal, India.&#x0D; A suitable site, meeting the orientation and the density of vegetation criteria, was chosen for collecting the primary data through an instrument survey. Ambient temperature, surface temperature and solar radiation data was collected from the thirteen cross-sections marked across the site. As part of the preliminary data processing, relative variations of ambient temperature, surface temperature and the solar radiation attenuation was calculated. The values pertaining to these three secondary parameters were used to form matrices corresponding to each of the trees of each of the cross-sections. These matrices were then compared with their corresponding matrices to estimate which side exhibited a higher cooling potential. Besides, a colour-coded matrix was also generated from the calculated shade index to evaluate the amount of incident solar radiation blocked by each of these trees.&#x0D; This study corroborates the previous findings that the shade providing trees can alter the microclimate of the area. Besides, it was also observed that the Northern pavements of the east-west oriented streets are cooler than the Southern pavement. Furthermore, it was also noticed that if the crown of the tree is present at a greater height from the ground, then lower solar angle results in less blockage of the incident solar radiation. Also, it was evident that a higher density of tall, shade providing trees, maximally block incident solar radiation on a street. This study can help in understanding the pattern of cooling potential of the street side vegetation in a warm and humid climatic zone like Kharagpur so that the plantation can be planned judiciously.

Extreme Temperature Exposure and Risks of Preterm Birth Subtypes Based on a Nationwide Survey in China.

Previous studies have reported that ambient temperature may affect perinatal outcomes. However, whether extreme temperature affects the risk of preterm birth (PTB) remains controversial. Studies on the associations of extreme temperature with PTB subtypes are lacking. We aimed to investigate the associations of extreme climate events with the risks of PTB and its subtypes, discerning possible modifiers. Data on all singleton deliveries were obtained from the China Labor and Delivery Survey (CLDS), a nationwide investigation implemented in 2015 and 2016. PTB was defined as gestational weeks and then categorized as early (24-34 wk) and late PTBs (35-36 wk), and clinical subtypes [spontaneous PTB, preterm premature rupture of the fetal membranes (PPROM), iatrogenic PTB]. Ambient temperature data were provided by the China National Weather Data Sharing System. Five heat indexes and five cold indexes were used to define heat waves and cold spells. Generalized linear mixed models with a random term by hospital unit were used to assess the associations of short-term prenatal extreme temperature exposure. The Cox proportional hazard regression model was applied to assess the nonlinear associations of low- or high-temperature exposure at the whole and different trimesters of pregnancy with the risk of PTB. Stratified analyses were conducted to assess the possible modification by geographic region and fetal sex. A total of 70,818 singleton births from 96 hospitals in China were included, among which 4,965 (7.01%) were PTBs. Exposure to extreme cold events 1 wk before delivery was associated with an increased PTB risk, with an adjusted odds ratio (aOR) [95% confidence intervals (CIs)] of 1.07 (95% CI: 1.04, 1.10) and 1.06 (1.04, 1.09) for the total days when the daily average temperature below the fifth percentile (fifth-days) and the 10th percentile (10th-days), 1.18 (1.04, 1.34) for the cold spells when the daily average temperature below the fifth percentile for two consecutive days (fifth-2D), 1.09 (1.03, 1.16) and 1.12 (1.06, 1.19) for the cold spells when the daily average temperature below the 10th percentile for three and two consecutive days (10th-3D and 10th-2D), respectively. Results of extreme temperature exposure during 2 weeks before delivery showed similarly significant associations. The association between cold spells and PTB tended to be stronger for late PTB than for early PTB. Cold spells were mainly associated with spontaneous PTB and late PPROM. A stratified analysis indicated that pregnant women in western and northern regions tended to be more sensitive to cold spells, and pregnant women with a female fetus appeared to be at a higher risk of PTB when exposed to cold spells. Pregnant women in late pregnancy were more susceptible to extreme temperatures. No significant or stable association was found between heat waves and preterm birth. Exposure to cold spells was associated with an increased risk of PTB, especially late, spontaneous PTB and PPROM. The associations appeared to be more pronounced in the north and west regions and in pregnancies with female fetuses. https://doi.org/10.1289/EHP10831.

Pre-processing study of multi-sensor data

For multi-sensor data with multi-source heterogeneity, data pre-processing is required for data fusion. This paper proposes a data pre-processing method, firstly, the image data is binarized using the designed image binarization algorithm and flame area extraction algorithm, and the flame area information in the fire image is extracted into one-dimensional data, then the five one-dimensional data of ambient temperature, temperature-sensitive cable, CO2, CO and flame area are noise reduced using the designed wavelet noise reduction algorithm, and finally the final pre-processed data is calculated by the selected normalization formula. The results of the study show that the method can improve the effectiveness of multi-sensor data fusion.

Synergies between China's Whole County photovoltaic program and rural heat pump adoption

AbstractElectrification of heating, particularly with highly‐efficient heat pumps, is increasingly viewed as essential for reaching the targets of the Paris Climate Agreement. China has promoted replacement of dirty coal heating in rural areas. More recently China has also begun promoting distributed solar photovoltaic (PV) energy as a rural development strategy, particularly with the launch of the Whole County PV pilot program in 2021. While several studies have examined the economics of heat pump adoption, with or without solar PV, the Whole County PV program has not been specifically studied. Further, many prior studies utilize monthly average solar production or temperature ranges. This study examines the economics of heat pumps in participating counties in the Whole County PV program, employing hourly data for both PV output and ambient temperature in participating counties. The analysis shows that pairing residential heat pumps with PV in counties in Shandong, Henan, and Jiangsu would result in short economic payback periods versus gas or resistance heat, while also increasing self‐consumption of PV. The results suggest that expanding the Whole County PV program to incorporate energy efficiency and heating/cooling measures represents an economically attractive way to accelerate the rural energy transition and improve rural livelihoods. Such a policy would help accelerate the low‐carbon energy transition, reduce air pollutant emissions, and help address oversupply of midday PV output in local areas. Barriers to the approach include involvement of different government ministries, the cost of upgrading building insulation, the design of building codes, and the structure of electricity tariffs.This article is categorized under: Sustainable Energy &gt; Solar Energy Energy and Power Systems &gt; Electrification Cities and Transportation &gt; Buildings

Genetic algorithm-based analysis of heat production prediction in electronic devices

Abstract In daily production life, heat generation and dissipation of electronic devices are important issues concerning the safety of the devices. To enhance the prediction and analysis of heat production of electronic devices, this paper analyzes and studies the heat production and dissipation of electronic systems of different electronic devices by collecting their historical operating power, hot spot temperature, ambient temperature, and other data, and iteratively optimizes these data using genetic algorithms to seek the best temperature fitting curve according to the research progress of existing artificial intelligence algorithms. The experimental results show that the population genes are sufficiently optimized as the number of iterations increases. The prediction model established by the genetic algorithm has a global optimization-seeking ability, high prediction accuracy, relatively small absolute and relative errors, and a fast convergence rate. This model has practical feasibility and can play a good role in the operation and maintenance of electronic devices.

Deep learning-based reconstruction of missing long-term girder-end displacement data for suspension bridge health monitoring

The flexibility of a suspension bridge usually results in significant longitudinal displacement at the end of the main girder under the joint action of environmental factors and traffic loads. Fluctuation amplitude and accumulation of girder-end displacement (GED) are primary data for the performance evaluation of suspension bridge elements, including expansion joints, supports, and dampers. However, the performance of the bridge structural health monitoring (SHM) system is frequently deteriorated by the long-term continuous GED data loss or anomaly. In this study, this issue of the suspension bridge SHM system was resolved by proposing a deep learning-based framework for reconstructing the missing GED data. Under this framework, a long short-term memory (LSTM) network-based regression model was first built between the ambient temperature data and the thermal-induced low-frequency components of GED data. Next, a U-net-based spectral bandwidth expansion model was applied for the step-by-step generation of vehicle/wind-induced high-frequency GED data terms based on the available low-frequency ones. Finally, a statistical correction strategy was employed to improve the prediction accuracy for the high-frequency GED data fluctuation amplitudes. The trained model for the reconstruction of the missing GED data uses only the air temperature input values in the data loss period. This method is especially lucrative when GED data in all sensor channels are missing. The reliability and efficiency of the proposed model were demonstrated by a case study. Specifically, the problem of reconstructing the missing GED data in the SHM system of the Jiangyin Yangtze River Bridge in China was considered. Using the relative error of cumulative GED as the performance indicator, a reconstruction precision within 10% was obtained.

Simplifying the process to perform air temperature and UHI measurements at large scales: Design of a new APP and low-cost Arduino device

The main purpose of Urban Heat Island (UHI) studies and the monitoring of ambient temperatures at large scales is to gather temperature information in a certain area, so as to understand temperature heterogeneity and its drivers or detect locations that lack thermal comfort. However, these studies originate mostly from some research projects, with sufficient budget and manpower to carry out the measurements and the necessary calculations. Thus, there is a scarcity of air temperature information, making it difficult to implement appropriate mitigation actions worldwide.The present work aims to greatly simplify the process to monitor air temperatures and carry out UHI estimations through mobile transects. This is done by introducing the design of a low-cost device and a new APP, which performs automatically most necessary UHI calculations. A web tool based on the Inverse Distance Weighting interpolation method is also included to obtain heat maps. The methodology is illustrated using case studies in Seville (Spain) and New York city (USA). By introducing a straightforward and reproducible methodology, we aim to open the path for a more widespread availability of ambient temperature data anywhere in the world, without the need for costly equipment and many hours of dedication by the researchers.

Optimal capacity of solar photovoltaic and battery storage for grid‐tied houses based on energy sharing

AbstractThis paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses.

Inter-seasonal characterization and correlation of Surface Urban Heat Island (SUHI) and Canopy Urban Heat Island (CUHI) in the urbanized environment of Delhi

The Intergovernmental Panel on Climate Change's (IPCC) 6th assessment report warns India about excessive heat. As India's population, urbanization, built-up regions, and concrete expand, vulnerable people may face higher heat risks. Delhi, India's capital and second-largest city, is noted for its excessive heat and air pollution. Heat in Delhi is spatially distributed and varies in magnitude due to built-up area density, the absence of a natural landscape, and other geographical aspects. To pinpoint the hotspot region, the study attempts to characterize Surface Urban Heat Islands (SUHIs) and Canopy Urban Heat Islands (CUHIs). Temporal and seasonal assessment of LST (Land Surface Temperature) using MODIS (Moderate Resolution Imaging Spectroradiometers) data and Ambient Temperature (AT) data from 39 Central Pollution Control Board (CPCB) monitoring stations with a representative radius of 1–5 km used to explore the inter-seasonal variation of SUHI and CUHI over the city in terms of magnitude and extent for 2019, 2020, and 2021. To describe SUHI and CUHI, a two-dimensional Gaussian fitting-based image analysis method is developed. The results show that the region's greatest SUHI magnitude is 2.36 °C in the monsoon season and 2.09 °C in the spring season. In comparison, the maximum CUHI magnitude is 4.74 °C and 3.15 °C in the Autumn and Winter seasons, respectively. The impact of SUHI can be experienced up to 82 km2 from the critical location in Winter and CUHI has 67.83 km2 in Summer. Inter seasonal comparison matrix between SUHI and CUHI demonstrates a positive but modest association due to considerable seasonal parameter fluctuation. UHI hotspot zone is at the centre of the urban with little seasonal fluctuation. South-West (SW) is a hotspot for SUHI and CUHI during the monsoon and spring seasons due to its dense residential and industrial areas. During winter, SUHI indicates SW as the critical zone and CUHI depicts NW. The finding shows that Delhi has a longer duration of hot weather. This study could help to understand UHI in Delhi at the local scale. The study shows UHI's seasonality and potential drivers. Critical hotspots and seasonal shifts should be used in urban development to improve Delhi's thermal environment.

Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety

Learning in asynchronous online environments has gained importance over the last several decades, and educational environment shifts from the COVID-19 pandemic appear to have increased this need. Science educators and students need information about which approaches work in the asynchronous environment where informal feedback tends to be reduced, compared to other teaching modalities. In this study, we asynchronously implemented a learning module across 5 institutions that guided students (N = 199) from prescriptive data analysis through guided inquiry and eventually to open inquiry. The module focuses on the science behind climate change. Students work with the same authentic data sets used by professional scientists to examine geologic history and causes of climate change. By analyzing contemporary atmospheric carbon dioxide and temperature data and then using the 800,000-year record available from the Vostok ice core proxy record of atmospheric properties, students identify the causes of climate change and discover the unprecedented nature of recent atmospheric changes. Using a pre/post-module assessment, we demonstrate improvement in students’ understanding of climate change processes and statistical methods used to analyze data. However, there was no evidence that the module develops students’ scientific reasoning about the relationship between causation and correlation. Students maintained that correlation is not causation, even when a robust causal mechanism (i.e., the greenhouse effect) explains the link between atmospheric carbon dioxide and temperature. Finally, our analysis indicated that generally, anxiety about climate change was reduced during the module, such that students become less anxious about the climate change the more they learn about it. However, science-denying students experienced much higher anxiety about climate change than students who accepted the scientific consensus about climate change. Climate science-dissenting students were so few in this study that a statistical comparison was not possible, but this intriguing finding warrants further investigation of the role of anxiety in science denial. Mainly, this study demonstrates how asynchronous online learning environments can indeed support the achievement of learning objectives related to conducting authentic science, such as increasing understanding of climate change and statistical concepts, all while not provoking anxiety about climate change.