Increase In Minimum Temperature Research Articles

Simulation study on additive-promoted microwave dehydration of lignite

ABSTRACT Microwave dehydration technology has been widely used in coal processing methods due to its unique advantages. Adding additives with high dielectric loss to coal can enhance the microwave absorption capacity of coal, so as to effectively improve the dehydration process. In order to understand the change and distribution of multi-fields in the microwave drying process of lignite under the action of different additives, verify the experiment and reduce the cost and number of experiments, this paper established a multi-field coupling model of electromagnetic field and heat and mass transfer of multiphase porous media based on COMSOL Multiphysics, so as to simulate the microwave drying process of lignite. The results show that the distribution areas of electromagnetic field, temperature field, and vapor in coal are basically the same, while the distribution of liquid water is just the opposite. With the enhancement of additive promotion effect, the parts with strong electromagnetic field strength increase, the hot spots of temperature field increase, the maximum temperature, and minimum temperature increase, and the water content in coal samples decreases. The promotion effect of additives on the microwave dehydration process of lignite is consistent with the experiment.

A century of medical records reveal earlier onset of the malaria season in Haut-Katanga induced by climate change

BackgroundDespite worldwide efforts to eradicate malaria over the past century, the disease remains a significant challenge in the Democratic Republic of the Congo (DRC) today. Climate change is even anticipated...

Model-based analysis of the impact of climate change on hydrology in the Guayas River basin (Ecuador)

ABSTRACT Worldwide climate change will most likely lead to drastic changes in hydrology and food production. In this study, the impact of climate change on the hydrological regime and the fate of pesticides in the Guayas River basin is investigated using the Soil and Water Assessment Tool. Four general circulation models and three representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) for three future periods were used to assess impact of climate change. Future projections showed a maximum increase in the average monthly precipitation of 40% in June, as well as an increase in an average minimum temperature of 3.85°C for July and an average maximum temperature of 4.5°C for August in 2080s. The model simulations based on RCP 8.5 scenario predict an increase in potential evapotranspiration by 11%, surface runoff of 39% and water yield of 33% in 2080s. The pesticide simulation showed the highest water concentrations during the wet season. Projections of trends in pesticide concentration indicate a similar trend to the current situation given the application rate remains the same. The results can be beneficial for the management and planning of the basin to mitigate flood and water quality-related impacts of food production and climate change.

Asymmetric Warming of Day and Night Benefits the Early Growth of Acer mono Seedlings More Than Symmetric Warming.

Asymmetric warming refers to the difference between the increase in daytime maximum temperature and the increase in nighttime minimum temperature and has been documented in temperate regions. However, its impacts on seedling growth have been largely ignored. In this study, seedlings of a widely distributed tree species, Acer mono Maxim., were exposed to both symmetric warming (SW) and asymmetric warming scenarios (day warming [DW], night warming [NW] and diurnal asymmetric warming [DAW]). Compared to control, all warming scenarios were found to enhance belowground biomass. DW promoted the seedling growth, while NW reduced the stem biomass. DAW did not impact the total biomass relative to the control. Compared to SW, DAW advanced phenology, increased indole-3-acetic acid content and chlorophyll content, which enhanced total biomass and stored more NSC in the root. Future DAW would be not beneficial to the growth of A. mono seedlings by comparing with the control. This research encourages further exploration of tree growth experiments under asymmetric warming conditions, as most studies tend to underestimate the warming effects on plant growth by focusing on SW. Incorporating the responses of seedling physiology and growth to non-uniform diurnal warming into earth system models is crucial for more accurately predicting carbon and energy balances in a warmer world.

Investigating the Impact of Climate Change Impacts on Direct-seeded Rice Production in Middle Gujarat Using the InfoCrop-rice Model

Rice (Oryza sativa L.) is a global food staple, essential for human nutrition and calorie intake, particularly in low- and middle-income countries. This study assesses the impact of climate change on direct-seeded rice (DSR) production in Middle Gujarat (India) using the InfoCrop-Rice model. The study evaluates potential climate scenarios under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, focusing on projected changes in temperature, rainfall, and sunshine hours, and their effects on rice yield and phenology. Field data from Kharif 2023 were used to calibrate the model, which projected significant increases in maximum and minimum temperatures, variable changes in rainfall, and consistent reductions in sunshine hours. By 2100, maximum temperatures are expected to rise by up to 6°C under RCP 8.5, and minimum temperatures could increase by as much as 7.5°C. Rainfall projections show high variability, with potential reductions and increases depending on the scenario and time period. Sunshine hours are projected to decline by approximately 6.6% under both RCP scenarios. The model simulations indicate a notable decline in rice yields, with reductions ranging from 18% to 36% by 2100. These results underscore the severe impact of climate change on DSR productivity. The study highlights the need for adaptive strategies, including improved water management, resilient crop varieties, and sustainable agricultural practices, to mitigate the adverse effects of climate change and ensure the sustainability of direct seeded rice production in Middle Gujarat.

Climate Change Trends And Its Effects On The Production Of Selected Food Crops: Evidence From Amaro Kelle District Of Southern Ethiopia

Amaro Kelle district of Southern Ethiopia is a rainfall-based agricultural districts that is vulnerable to the impacts of climate change and risk. This study analyzed climate change trends and effects on the selected food crops production: evidence from the Amaro Kelle district of Southern Ethiopia. Secondary data on climate change trends and food crop production from the Ethiopian national meteorological agency and the Amaro Kelle agricultural and rural development office were used for the study. Microsoft Excel was used to analyze the data on temperature, rainfall and crop production trends. The result on Trend analysis showed an increase in maximum and minimum temperatures at the rate of 0.056 and 0.0787 0C per 32 years, while there is a decrease in annual rainfall at the rate of -7.6012 mm per 32 years. These climatic conditions increased maize and sorghum productivity at the rate of 713.69 and 376.56 tonnes per 12 years, respectively, and decreased productivity of teff and wheat at the rate of -2618.5 and -30.892 tonnes per 12 years, respectively. These findings indicated that climate change positively and negatively affected the district's food crop production and food security. Finally, the study recommends that farmers in the district should focus on the cultivation of those crops that correlate positively with increasing temperature and decreasing rainfall trend in the study area.

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

In-depth Exploration of Temperature Trends in Morocco: Combining Traditional Methods of Mann Kendall with Innovative ITA and IPTA Approaches

This study examines trends in minimum and maximum temperatures at various climate stations located in different regions of Morocco for a period of five decades (1970 to 2019). Mann–Kendall, Sen’s estimator, Innovative Trend Analysis (ITA) and Innovative Polygon Trend Analysis (IPTA) were used in the analysis. The results show significant fluctuations, at different time scales, between minimum and maximum temperatures at all stations. In coastal areas, such as Rabat Sale, minimum temperatures fell during January and February while other months saw increases. Average minimum temperatures in Rabat Sale tend to fall by 0.5 °C. On the other hand, maximum temperatures in Rabat Sale rose by 0.2 °C. A decrease of 0.4 °C for Tmin and 1.6 °C for Tmax were observed in higher continental regions, such as Meknes. Other stations, such as Fez Sais (0.6 °C Tmin and 2.6 °C Tmax) and Taza (1.1 °C Tmin and 2.6 °C Tmax) showed an upward trend. Trends also vary, with notable increases in minimum and maximum temperatures, indicating different climatic dynamics according to altitude and locality. In particular, the ITA highlights a significant increase in annual maximum temperatures, with a P-value < 0.05 and trend slopes ranging from 0.0015 °C per year in Rabat Sale to 0.0076 °C per year in Taza. In addition, the IPTA results confirm diversity of upward and downward trends on monthly and seasonal scales, highlighting impact of geographical factors such as proximity to sea, topography, and continentality that contribute to formation of regional microclimates. The results highlight significant impact of climate change in Morocco.

The Effect of Climate Change on the Water Requirements of Wheat Crops in Al-Qurna District for the Periods (1950-1961) and (2011-2022)

Climate is one of the fundamental factors influencing various human activities, including agriculture. Climatic conditions impact the type of crops that can be grown and their water requirements, which is especially important in arid regions like the study area. Understanding these requirements helps determine the amount of irrigation water needed for crops to avoid overuse, as well as the agricultural area that can be irrigated and the water needs of wheat crops. Water consumption refers to the amount used by a crop during its growth period over a specified area to build plant tissues within a specific timeframe. The study shows that climatic elements such as solar radiation, various temperature types (mean, maximum, and minimum), wind, relative humidity, rainfall, and evaporation affect the water requirements of wheat crops in the study area. When examining the climate element changes between the periods 1950-1961 and 2011-2022, whether the change direction is positive (increase) or negative (decrease), it was observed that there were increases in solar radiation, mean temperature, maximum and minimum temperatures, and evaporation. Meanwhile, there were decreases in relative humidity, rainfall, and wind speed, with an increase in water demand for all wheat planting months, especially in October, March, and April during the 2011-2022 period. This is due to higher evaporation resulting from increased temperatures, unlike the 1950-1961 period , during which the water demand for most months was lower due to moderate climate elements.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model.

The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

An Evaluation of Dryland Ulluco Cultivation Yields in the Face of Climate Change Scenarios in the Central Andes of Peru by Using the AquaCrop Model

Ullucus tuberosus is an Andean region crop adapted to high-altitude environments and dryland cultivation. It is an essential resource that guarantees food security due to its carbohydrate, protein, and low-fat content. However, current change patterns in precipitation and temperatures warn of complex scenarios where climate change will affect this crop. Therefore, predicting these effects through simulation is a valuable tool for evaluating this crop’s sustainability. This study aims to evaluate ulluco’s crop yield under dryland conditions at 3914 m.a.s.l. considering climate change scenarios from 2024 to 2100 by using the AquaCrop model. Simulations were carried out using current meteorological data, crop agronomic information, and simulations for SSP1-2.6, SSP3-7.0, and SSP5-8.5 of CMIP 6. The results indicate that minimum temperature increases and seasonal precipitation exacerbation will significantly influence yields. Increases in rainfall and environmental CO2 concentrations show an opportunity window for yield increment in the early stages. However, a negative trend is observed for 2050–2100, mainly due to crop temperature stress. These findings highlight the importance of developing more resistant ulluco varieties to heat stress conditions, adapting water management practices, continuing modeling climate change effects on crops, and investing in research on smallholder agriculture to reach Sustainable Development Goals 1, 2, and 13.

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000−2020) and future periods (2021–2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

Extreme climate events and future population exposure under climate change in the Huaihe River basin of China based on CMIP6 multimodel ensembles projections

AbstractThe Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (&gt;25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.

The association between climate variables and tuberculosis in Kolaka District, Southeast Sulawesi Province, Indonesia, 2013-2020: a Bayesian autoregressive model.

Tuberculosis is one of the diseases that requires comprehensive treatment. This disease is highly contagious and can be transmitted through the air. Climate factors play a role in the increasing cases of tuberculosis. This study aimed to determine the correlation between climatic variables and TB in Kolaka District, Southeast Sulawesi Province, Indonesia. This research was modeled using an autoregressive (AR) Bayesian model with three possible likelihoods; Gaussian, Poisson and Negative Binomial responses. Minimum temperature and average temperature, a coefficient of 4.234 suggests that for every 1 degree increase in minimum temperature, there is an estimated increase of approximately four cases, assuming other variables remain constant. Maximum temperature, a coefficient of 17.851 suggests that for every 1 degree increase in maximum temperature, there is an estimated increase of around 17-18 cases, assuming other variables remain constant. Humidity, a coefficient of -13.413 suggests that for every 1% increase in humidity, there is an estimated decrease of around 13 cases, assuming other variables remain constant. Rainfall, a coefficient of -0.327 suggests that for every 1 mm increase in rainfall, there is an estimated decrease of around 0.327 cases, assuming other variables remain constant. Light, a coefficient of -4.322 suggests that for every 1-hour increase in light duration, there is an estimated decrease of around four cases, assuming other variables remain constant. Climate change has a significant impact on tuberculosis through temperature-related factors. These factors influence the prevalence, spread, and vulnerability to TB. Addressing these challenges requires a holistic approach involving adaptation planning. Strong public health systems and healthcare infrastructure can help mitigate the risks and impacts of climate change-related tuberculosis.

Unravelling the impact of microclimatic changes on coastal aquifer of multiple land use through integrated techniques: A comparative study using two decadal data

Regional climatic changes reflect the microclimatic variations and these affect the water resources of a region. Urbanization and population growth significantly influence microclimates, impacting groundwater resources and Land Surface Temperature (LST). The climatic variables (rainfall and temperature) interplay with geospatial parameters such as Land Use Land Cover (LULC), LST influence the quality and quantity of groundwater. Hence, this current approach employs geospatial techniques to explore the interrelationship between these variables by studying the long-term variation for a period of 20 years (1997–2018) to assess the microclimate variation and its impact on groundwater resources of a coastal aquifers with complex lithology. Results reveal a notable increase in minimum and maximum temperatures, with an average annual rise of 0.5°C predominantly along the western part. Spatial rainfall variation for the study period was higher in the northern region influenced by urban heat islands. The change in LULC reflected a 12% increase in agricultural areas and a 1.7% expansion in habitation, accompanied by a decline in surface water bodies in 2018. Maximum increase in electrical conductivity(EC) is observed along the southern coastal region, indicating rise in ionic concentration due contamination from sea water, urban and agricultural sources, witnessed by the isotopic signatures. Comparison of the depth to groundwater between the different study periods indicate a maximum depletion of 49m in northeastern and western regions due to extraction for domestic water supply, agriculture and mining. Further, a maximum rise of 14m in the regions adjacent to the reservoirs was observed, during 2018. The current study reveals the relationship between microclimate and groundwater (quality and the quantity) is predominantly observed in the northern and western parts of the study area. Our findings suggest that managing groundwater extraction and improving land use practices are essential for sustainable development, primarily through policy and governance.

Comprehensive evaluation of extreme hydrometeorological events coincidence and their interrelationships in the Hanjiang River Basin, China

Extreme hydrometeorological events are often highly destructive and can cause huge damage to the natural environment and social economy. As climate change and human activities intensify, the frequency and intensity of such extreme events continue to increase. This phenomenon is also shown in the Hanjiang River Basin (HRB) in recent decades. In this study, we focus on the coincidence occurrence patterns of extreme hydrometeorological events. First, we used the standardized index method to extract the annual maximum and minimum values of runoff, precipitation, maximum and minimum temperatures from 1961 to 2019 in the control area of the Baihe (BH) hydrological station, with time scales from 1 month to 12 months. Then through the Copula and nested structure Copula functions, the joint occurrence probability of bivariate and multivariate were analyzed. The results show that too high a maximum temperature will inhibit runoff, while an increase in minimum temperature will promote an increase in runoff to a certain extent. Both the maximum and minimum temperatures in the basin shown a significant increasing trend, and the intensity of hydrological drought also shows an increasing trend. An increase in maximum temperature will aggravate hydrological drought, as the combined joint occurrence probability of them is the greatest. Analysis through the nested structure Copula functions show that when extreme hydrometeorological events both occurred at the median of their intervals, their coincidence occurrence probability is the maximum, and when the time scale is eight months, their coincidence occurrence probability is the highest. This study can provide a reference for joint probability analysis of extreme hydrometeorological events.

Analysis of tropical nights in Spain (1970–2023): Minimum temperatures as an indicator of climate change

AbstractIn the current context of climate change, the increase in minimum temperatures recorded in recent decades has received special scientific attention due to its importance for the proper night's sleep and health of the population, among other considerations. One of the main indicators usually considered refers to the frequency of tropical nights (≥20°C), which have begun to become widespread in regions hitherto excluded from this type of event. This paper analyses tropical nights in Spain between 1970 and 2023, addressing their mean annual occurrence, their intensity, their monthly distribution and the average number of consecutive tropical nights recorded, as well as their relationship with relative humidity. The results, based on the analysis of 75 homogenized series located in 44 different provinces, allow us to differentiate seven large areas based on the minimum temperature in which there has been an increase in the frequency and intensity of tropical nights, which progressively extend the season in which they can appear. Similarly, a generalized increase in the maximum number of consecutive nights thermally above 20°C was identified, in addition to the presence of high relative humidity in coastal areas during these episodes.

Unveiling Climate Trends and Future Projections in Southeastern Brazil: A Case Study of Brazil’s Historic Agricultural Heritage

The intricate relationship between climate and society in a given region demands a profound understanding of climate patterns, especially in agricultural areas like Diamantina, Minas Gerais (MG), recognized by the Food and Agriculture Organization (FAO) as the birthplace of the first Globally Important Agricultural Heritage System (GIAHS) in Brazil, situated in the southwest region of the country. Given the growing concerns about climate change, we conducted a meticulous analysis of the climatic characteristics of Diamantina-MG. To achieve this, we examined historical meteorological data from 1973 to 2022, employing the Mann–Kendall and Sen’s slope tests to analyze trends. Additionally, we utilized three global climate models (GCMs) under different shared socioeconomic pathways (SSPs) to predict future climate scenarios (2021–2100) based on the projections of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we used Köppen and Thornthwaite climate classification methodologies to characterize both the current and future climate conditions of the region. Our results indicate that, historically, Diamantina-MG has experienced significant increases in minimum temperature, indicating a warmer climate in recent decades. For temperature, the projections show a consensus among models, projecting a continuous increase, potentially reaching up to 5.8 °C above the historical average temperature (19.2 °C) by the end of the century. Regarding rainfall projections, they show greater uncertainty, with discrepancies among models observed until 2060. However, specifically for the second half of the century (2060–2100), the models agree that there will be increases in annual rainfall. Regarding the climatic types of the region, we found that the current Köppen Cwb and Thornthwaite B3rB’3a’ classifications could shift to Aw and B1wA’a’, representing a humid tropical savanna climate with longer periods of water deficiency, considering the impacts resulting from increased air temperature and evapotranspiration. In summary, the study’s results indicate that climate changes are occurring and are likely to intensify in the Jequitinhonha Valley region, MG, in the future. The analysis of these data, from the perspective of the Brazilian GIAHS sustainability, reveals the importance of considering adaptation and mitigation measures to ensure the resilience of agricultural systems and local communities in the region that face these significant environmental changes.

Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

ASSESSING THE DYNAMICS OF CLIMATE CHANGE IN KHULNA CITY: A COMPREHENSIVE ANALYSIS OF TEMPERATURE, RAINFALL, AND HUMIDITY TRENDS

The study analyzes the temperature, rainfall, and humidity dynamics of Khulna city from 1981 to 2020. The average rainfall changes are 0.0018 mm, with an increase in daytime temperatures of 0.0223°C each year. Nighttime temperatures also show an increasing trend, with an increase in minimum temperatures of 0.0342°C each year. The overall humidity decreases, indicating less humid weather. The decadal average rainfall ranges from 5.13 mm from 1981 to 2000, with a drop to 4.65 mm between 1991 and 2000. The maximum temperature ranges from 30.94°C to 31.31°C, with a slight increase to 22.17°C between 2011 and 2020. The decadal average humidity ranges from 81.28% from 1991–2000 to 80.72% from 2001–2010. Pre-monsoon average rainfall declines by 5.13 mm, indicating a drier season. The monsoon season has an inclining trend of 0.0239 mm, with a promising increment of rain resulting in a wetter monsoon. Post-monsoon average rainfall increases by 0.0121 mm, resulting in a wetter season each year. The winter season has a slight decline of -0.0043 mm at 6°C. The variability of temperature, rainfall, and humidity patterns in Khulna city reveals a correlation between rainfall and temperature, which indirectly impacts crop yield. Better observational rainfall, humidity, and temperature data are necessary for effective agriculture and crop production. The estimated value for the average temperature (maximum) from 1981 to 2020 is 0.023°C, suggesting that if the year increases by one year, the average maximum temperature increases by 0.023°C.