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The effect of bad air quality on human health is a well-known risk. Annual health costs have significantly been increased in many countries due to adverse air quality. Therefore, forecasting air quality-measuring parameters in highly impacted areas is essential to enhance the quality of life. Though this forecasting is usual in many countries, Sri Lanka is far behind the state-of-the-art. The country has increasingly reported adverse air quality levels with ongoing industrialization in urban areas. Therefore, this research study, for the first time, mainly focuses on forecasting the PM10 values of the air quality for the two urbanized areas of Sri Lanka, Battaramulla (an urban area in Colombo), and Kandy. Twelve air quality parameters were used with five models, including extreme gradient boosting (XGBoost), CatBoost, light gradient-boosting machine (LightBGM), long short-term memory (LSTM), and gated recurrent unit (GRU) to forecast the PM10 levels. Several performance indices, including the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute relative error (MARE), and the Nash–Sutcliffe efficiency (NSE), were used to test the forecasting models. It was identified that the LightBGM algorithm performed better in forecasting PM10 in Kandy (R2=0.99, MSE =0.02, MAE=0.002, RMSE =0.1225, MARE =1.0, and NSE=0.99). In contrast, the LightBGM achieved a higher performance (R2=0.99, MSE =0.002, MAE =0.012 , RMSE =1.051, MARE =0.00, and NSE=0.99) for the forecasting PM10 for the Battaramulla region. As per the results, it can be concluded that there is a necessity to develop forecasting models for different land areas. Moreover, it was concluded that the PM10 in Kandy and Battaramulla increased slightly with existing seasonal changes.

Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.

Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset produced by the European Centre for Medium-range Weather Forecasts (ECMWF). The data covered the period from 2008 to 2017. The bulk Richardson number methodology was used to determine the PBL height (PBLH). The PBLH obtained from the ERA5 data agreed well with that derived from sounding and Global Positioning System Radio Occultation datasets. Significant diurnal and seasonal variability in PBLH was observed. The PBLH increases from morning to late afternoon, decreases in the evening, and is lowest at night. It is high in the summer, lower in spring and autumn, and lowest in winter. The variability of the PBLH with respect to temperature, relative humidity, surface pressure, wind speed, lower tropospheric stability, soil moisture, and surface fluxes was also examined. The growth of the PBLH was high in the spring and in southern regions due to the low soil moisture content of the surface. A high PBLH pattern is evident in high-elevation regions. Increasing trends of the surface temperature and accordingly PBLH were observed from 2008 to 2017.

The temporal and spatial variability of oceanographic properties in Potter Cove was analysed for the 2010–17 summer periods. This was linked with meteorological parameters and sea ice. The water column structure presented significant differences in turbidity between two areas (away from and closer to the Fourcade Glacier). The recent retreat has been transforming it into a land terminating glacier. Therefore, correlations obtained between oceanographic properties near the glacier and meteorological parameters reveal that atmospheric conditions are the main forcing of the Potter system, in agreement with previous studies. Also, high turbidity values within deeper waters in 2013 and 2014 were probably related to resuspended glacial sediment input into the cove. Interannual variability observed in the local parameters was connected to ENSO and SAM, reflecting a larger connection with ENSO, mainly in longer timescales. Colder waters during the 2010 and 2016 El Niño phases could be related to lower air temperature. In summer 2010 during a negative SAM phase, colder, more saline and low turbid waters were observed. Alternatively, in 2012 during La Niña and positive SAM, warmer, fresher and more turbid conditions were found with high vertical stratification. Finally, during 2015 (positive SAM), warmer and low salinity waters were observed.

During the pre- and post-monsoon season, the eastern and western coasts are highly vulnerable to cyclones. The tropical cyclone “Tauktae” formed in the Arabian Sea on 14 May 2021 and moved along the west coast of India, and landfall occurred on 17 May 2021. During the cyclone, the maximum wind speed was 220 km/h with a pressure of 935 mb affecting meteorological, atmospheric parameters, and weather conditions of the northern and central parts of India causing devastating damage. Analysis of satellite, Argo, and ground data show pronounced changes in the oceanic, atmospheric, and meteorological parameters associated during the formation and landfall of the cyclone. During cyclone generation (before landfall), the air temperature (AT) was maximum (30.51 °C), and winds (220 km/h) were strong with negative omega values (0.3). The relative humidity (RH) and rainfall (RF) were observed to be higher at the location of the cyclone formation in the ocean and over the landfall location, with an average value of 81.28% and 21.45 mm/day, respectively. The concentration of total column ozone (TCO), CO volume mixing ratio (COVMR), H 2 O mass mixing ratio (H 2 O MMR), aerosol parameters (AOD, AE) and air quality parameter (PM) was increased over land and along the cyclone track, leading to a deterioration in the air quality. The strong wind mixes the air mass from the surroundings to the local anthropogenic emissions, and causing strong mixing of the aerosols. The detailed results show a pronounced change in the ocean, land, meteorological, and atmospheric parameters showing a strong land–ocean-atmosphere coupling associated with the cyclone.

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry.

Meteorological parameters, as the important basis for improving building energy efficiency, will show apparent changes under climate change conditions. In this study, outdoor design temperature and humidity for air‐conditioning system in 12 cities, representing major climatic zones of China, were analysed to determine the impacts of temperature and humidity on the design load of air‐conditioning system. The results showed that outdoor design temperature largely increased during the last few decades and relative humidity apparently decreased except for two cities belonging to the severe cold and hot summer and cold winter climate zones. The increase in temperature and decrease in humidity have made obvious changes in the design load of air‐conditioning system. The design loads for cooling increased 1.8%–10% in these selected cities during 1991–2017 compared with 1961–1990. By contrast, the design loads for dehumidification decreased 0.8%–7% in 1991–2017 relative to 1961–1990. When the cooling and dehumidification were considered together, the design loads changed from −1.5% to 4.1% in the two periods, depending on cities or climate zones. These results demonstrated that, under the condition of climate change, the combined changes of humidity and temperature rather than temperature alone should be fully considered to determine the design capacity of air‐conditioning system, especially for temperature and humidity independent control (THIC) air‐conditioning system, to save energy and improve indoor comfort. Design loads for air‐conditioning systems of office buildings under climate change in different climate zones of China were determined by separating the impacts of temperature and humidity on design loads. The present study confirmed that the changes of humidity and temperature and their impacts rather than the single temperature should be fully considered to determine the design capacity of air‐conditioning system, especially for temperature and humidity independent control air‐conditioning system, to save energy and improve indoor comfort.

Three measurement campaigns were conducted on the island of Culuccia (Sardinia, Italy) to evaluate particulate matter (PM) concentrations and the contribution of sea spray aerosol (SSA) across different seasons in a largely uncontaminated coastal environment. The goal is not only to analyze PM concentration in relation to meteorological parameters such as temperature, relative humidity (rH), and wind speed but also to provide a chemical analysis of SSA. The chemical composition of PM was determined using Raman spectroscopy and SEM-EDX, allowing for precise identification of individual particles. Results showed seasonal variations in PM composition, with sodium nitrate and sodium chloride prevalent in March and June and sulfates dominating in October. A correlation between the PM composition and meteorological parameters was observed according to the value of the deliquescence relative humidity (DRH), highlighting the reciprocal influence of rH and coarse and fine PM trends. This multi-technique approach offers valuable insights into the relative abundance of different PM compound classes based on the varying conditions for SSA formation. This enhances our understanding of the behavior of sea spray aerosol and other PM in natural coastal environments.

Since 2016, the Ministry of Ecology and Environment and the Beijing Municipal Government have adjusted the minimum concentration limit for ambient air dustfall several times, indicating that they attach great importance to dustfall. To grasp the pollution characteristics and sources of dustfall, in this work, the filtration method was used to determine the insoluble dustfall and water-soluble dustfall in the urban area of Beijing. From our analysis, the influence of the meteorological parameters on dustfall was found, and the chemical components of dustfall were determined. The positive matrix factorization (PMF) model was also utilized to analyze the sources of dustfall. The results indicated that the average amount of dustfall in 2021–2022 was 4.4 t·(km2·30 d)−1, and the proportion of insoluble dustfall deposition was 82.4%. Dustfall was positively correlated with the average wind speed and temperature and negatively correlated with the relative humidity and rain precipitation. The impact of the meteorological parameters on insoluble dustfall and water-soluble dustfall was the opposite. The average proportions of crustal material, ions, organic matter, element carbon, trace elements, and unknown components were 48%, 16%, 14%, 1.4%, 0.20%, and 20%, respectively. The proportions of the crustal material and ions were the highest in spring (57%) and summer (37%). The contribution rates of fugitive dust source, secondary inorganic source, mobile source, coal combustion source, snow melting agent source, and other sources were 42.4%, 19.3%, 8.3%, 3.0%, 2.7%, and 24.3%, respectively. This study supported dustfall pollution control by analysing the pollutant characteristics and sources of dustfall from the standpoint of total chemical components. In order to better control dustfall pollution, control measures and evaluation standards for fugitive dust pollution should be formulated.