• Energy Research

Land Surface Temperature (LST) is an important factor in ground surface energy balance and in universal climatology studies. Elevation, Land Cover (LC), and vegetation index are three factors that influence ground surface variation, and their influences vary depending on geography. This study aimed to: (i) investigate the seasonal patterns and trends of daytime LST, and (ii) examine the influence of elevation, LC, and vegetation index on daytime LST increase in Taiwan from 2000 to 2021. LST, vegetation, and LC data were downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS) website, and elevation data were downloaded from the United States Geological Survey (USGS) website. The natural cubic spline method was applied to investigate annual seasonal patterns and trends in daytime LST. Linear regression modeling was applied to investigate the influence of elevation, LC, and vegetation index on daytime LST increases. The results showed that the average increase in daytime LST per decade in Taiwan was 0.021 °C. Elevation, LC, and vegetation had significantly affected the daytime LST increase, with R2 of 32.5% and 28.1% for the North and South parts of the country, respectively. The daytime LST increase in the North at elevations higher than 1000 m had an increasing trend, while in the South the increasing trend was found at elevations higher than 350 m above sea level. All types of forest and urban areas in the North had a higher daytime LST increase than the average, while in the South, the areas with water, closed shrubland, and urban parts had a higher daytime LST increase than the average.

AbstractThe transitivity of solar radiation in the atmosphere varies greatly depending on location, time of day, earth-to-sun distance, angle of incidence, and other variables. Solar radiation has an impact on climate change and can be used as energy. So, its modelling will help plan and design policies for climate change and the sustainable use of energy. This study aimed to investigate solar energy patterns and trends on the Earth’s surface via solar radiation absorption by cloud cover. Data on solar radiation absorption from 133 stations between the years 1998 and 2020 across the United States were downloaded from the National Solar Radiation Database (NSRDB) website. A linear regression model was used to model solar absorption by cloud and factor analysis was used to group the regions by reducing the spatial correlation of solar radiation absorption. After that, a multivariate regression model was utilized to investigate average changes. There were seven regions obtained from factor analysis. All regions showed a seasonal pattern, with the peak in December to January and the lowest level in June to July. The north, north-east, or south-east of the country experienced an increase in solar radiation absorption, while the north-west, central, and south of the country experienced a decrease. The overall average absorption increased by 0.015%. The patterns and trends of solar radiation by location and time help climate scientists make better decisions. It is also useful to manage renewable energy sources, which will lead policymakers to make better policies.

Background: /Objectives: Air pollution seriously threatens human health; even low-level exposure can have negative consequences. The study aimed to explore the influence of air pollution on cardiorespiratory diseases, adjusting for climatic conditions. Methods: Poisson regression using a generalized additive model (GAMs) and a distributed lag non-linear model (DLNM) were used to explore the relationships between air pollution and cardiorespiratory illnesses. Results: Asthma, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and lung cancer were reported at 88 per 10,000 population. Annual PM10 and PM2.5 levels were higher than WHO guidelines. PM10 and PM2.5 appeared to be on an upward trend, while NO2 and SO2 appeared to be on a downward trend. The 10 % increase in PM10 was significantly associated with an increase in inpatient department (IPD) admissions for asthma at a lag of 12–13 days and lung cancer at a lag of 14–15 days, whereas PM2.5 was associated with an increase in IPD admissions for asthma at a lag of 10–14 days and 13–15 days, respectively. A 10 % rise in PM10 was associated with an increase in COPD inpatient admissions at lag 2–6 days, while PM2.5 was associated with an increase in cardiovascular inpatient admissions at lag 4–5 days. A 10 % increase in NO2 increased admissions to COPD at all 15 lags, whereas a 10 % increase in CO increased admissions to lung cancer at lags 9–15 days. Conclusions: The rise in PM2.5 and PM10 levels in this area leads to increased exposure to PM pollutants, hence elevating the likelihood of developing cardiorespiratory diseases.