Estimating Hourly Full-Coverage PM2.5 Concentrations Based On MODIS Data Over The Northeast of Thailand.

Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

Determination Of Groundwater Recharge Area In Raimanuk And Surroundings, Timor Island

The Raimanuk area in Timor, East Nusa Tenggara, is located in the Aroki Groundwater Basin. The decreasing quality and potential groundwater availability in the Aroki Groundwater Basin is feared due to its widespread use for household needs and agriculture. The lack of the groundwater recharge area map will pose an obstacle in policymaking regarding the management and preparation of spatial conservation areas in the Raimanuk Region. This study aims to determine the zone and classification of groundwater recharge areas in the Raimanuk area based on spatial data analysis. The groundwater recharge area can be determined using slope, river flow patterns, spring emergence, and groundwater table depth. The classification of the recharge area uses a scoring approach with an overlapping analysis of the parameter assessments, which are hydraulic conductivity, precipitation, soil cover, slope, and depth of unconfined groundwater. The result of the study is the groundwater recharge area map of Raimanuk. The groundwater recharge area is located in the Mandeu Hill area, which is the main recharge area. The groundwater discharge area is located in the Aroki plain area that can be the main recharge area.

Meridional Migration of Diurnal Heavy Rainfall during Extreme Events over Java and Surrounding Waters and Its Relation to Madden Julian Oscillation

In this study, meridional migration characteristic of diurnal heavy rainfall (DHR) over Java and surrounding waters and its relation to Madden Julian Oscillation (MJO) during extreme events was investigated. The rainfall data was the Climate Prediction Centre Morphing (CMORPH) V1.0 in the December-January-February (DJF) wet season of the 1998-2019 period. The thresholds of extreme events were based on the 95% percentile of daily rainfall area average of mountainous area (MA), northern plain area (NPA), northern waters (NW), southern plain area (SPA), and southern waters (SW) that were based on Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model. We analyzed meridional migration of DHR through composite and the Hovmoller diagram. To get the MJO signal, we used the Wheeler-Kiladis wavenumber-frequency domain to filter outgoing longwave radiation (OLR). The results showed that DHR was stationary over the mountains, migrated to the Java Sea (The Indian Ocean), and was stationary over the Java Sea (Indian Ocean) in conjuction with migration from Java to the waters when extreme events occur over MA, NPA (SPA), and NW (SW), respectively. Based on a comparison of MJO-OLR during extreme events period of MA, NPA, SPA, NW, and SW, it seems that MJO had a stronger impact on the DHR of NW and SW than the others, but it must be examined based on significant test in the further study.