The present study utilizes 18 years of long-term (2001–2018) data collected from six active AERONET sites over the Indo-Gangetic Plain (IGP) and the North China Plain (NCP) areas in Southeast Asia. The annual mean (±SD) aerosol optical thickness at 440 nm (AOT₄₄₀) was found high at XiangHe (0.92 ± 0.69) and Taihu (0.90 ± 0.51) followed by Beijing (0.81 ± 0.69), Lahore (0.81 ± 0.43), and Kanpur (0.73 ± 0.35) and low at Karachi (0.52 ± 0.23). Seasonally, high AOT₄₄₀ with corresponding high Ångström exponent (ANG₄₄₀₋₈₇₀) noticed during JJA for all sites, except Kanpur, suggesting the dominance of fine-mode particles, generally associated with large anthropogenic emissions. Climatologically, an increasing (decreasing) trend was observed over IGP (NCP) sites, with the highest (lowest) percentage of departures in AOT₄₄₀ found over Beijing (Karachi). We further identified major aerosol types which showed the dominance of biomass burning, urban-industrial followed by the mixed type of aerosols. In addition, single scattering albedo (SSA), asymmetry parameter (ASP), volume size distribution (VSD), and complex aerosol refractive index (RI) showed significant temporal and spectral changes, illustrating the complexity of aerosol types. At last, the annual mean direct aerosol radiative forcing at the top, bottom, and within the atmosphere for all sites were found in the range from −17.36 ± 3.75 to −45.17 ± 4.87 W m⁻², -64.6 ± 4.86 to −93.7 ± 10.27 W m⁻², and 40.5 ± 6.43 to 68.25 ± 7.26 W m⁻², respectively, with an averaged atmospheric heating rate of 0.9–2.3 K day⁻¹. A large amount of anthropogenic aerosols showed a significant effect of heating (cooling) on the atmosphere (surface) results obviously, due to an increased rate of atmospheric heating. Therefore, the thermodynamic effects of anthropogenic aerosols on the atmospheric circulation and its structure should be taken into consideration for future study over the experimental sites.

Particulate matter pollution of less than 10 μm in diameter (PM10) is a problem for some regional and urban centres across New Zealand during the winter period when solid fuel (wood and coal) fires are used for home heating. Elevated levels of PM10 concentrations occur during stable atmospheric conditions, when cool air temperatures and low wind speeds allow for a surface inversion to occur and trap PM10. This study examined the relationships between PM10 and local and large-scale synoptic conditions at daily and seasonal scales. Minimum temperature and wind speed were both negatively correlated with PM10 during the winter season, whilst the combination of the two can explain 30–54% of variability in average PM10. Synoptic-scale daily composites of high PM10 days showed the evolution of an anticyclone in the Tasman Sea, with an injection of cool air over New Zealand and persistent south-westerly winds leading to cold and stable conditions on the day of exceedance. Both of these results indicate that there is some potential for predicting days in which atmospheric conditions could favour elevated PM10 concentrations. Furthermore, at the seasonal scale, weaker westerly winds were found to be associated with winters with higher exceedance days, although the relationship is not straightforward. These characteristics can be associated with other, predictable large-scale climate drivers such as the El Niño-Southern Oscillation, and may aid in identifying years in which a higher risk of PM10 pollution events exists.

The study presents a baseline variability and climatology study of measured hydrodynamic, water properties and some water quality parameters of West Johor Strait, Singapore at hourly-to-seasonal scales to uncover their dependency and correlation to one or more drivers. The considered parameters include, but not limited by sea surface elevation, current magnitude and direction, solar radiation and air temperature, water temperature, salinity, chlorophyll-a and turbidity. FFT (Fast Fourier Transform) analysis is carried out for the parameters to delineate relative effect of tidal and weather drivers. The group and individual correlations between the parameters are obtained by principal component analysis (PCA) and cross-correlation (CC) technique, respectively. The CC technique also identifies the dependency and time lag between driving natural forces and dependent water property and water quality parameters. The temporal variability and climatology of the driving forces and the dependent parameters are established at the hourly, daily, fortnightly and seasonal scales.

Given the strong impact of air quality on health, environment, and economy, Morocco has implemented an air quality network to assess air pollutants including PM₁₀ (particulate matter with a diameter less than 10 μm). This network which is composed of 29 fixed measurement stations is spatially limited and does not provide sufficient time resolution. The scarcity of measured air quality data led to seek an optimal alternative source to conduct related data-based studies. This represents the primary objective of this paper. PM₁₀ concentrations of global Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) data (4D Variational analysis “4v” and analysis “an”), as well as regional CAMSRA data, were examined against the average daily PM₁₀ concentrations collected from six fixed Moroccan air quality measurement stations in 2016 (i.e., observation data). The verification is carried out by studying and analyzing seasonal, extreme, and annual values. The study shows a strong seasonal dependence with a positive bias in winter and a negative bias during summer. For the study of extreme values, global CAMSRA “an” and “4v” data record significant bias of approximately 184 and 161 μg/m³, respectively. However, the annual analysis shows that the CAMSRA global “an” data have the smallest average bias (20.008 μg/m³) and hence has the closest representation of observation data. We conclude that the CAMSRA global analysis data could be used to compute climatology, study trends, evaluate models, benchmark other reanalysis, or serve as boundary conditions for regional models for past periods.