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This study introduces a novel method for estimating hourly concentrations of particulate matter 2.5 μm (PM2.5) using satellite data. The pollution control department of Thailand collected hourly PM2.5 data nationwide in 2020. NASA's Earth Observing System Data and Information System encompasses all moderate resolution imaging spectroradiometer satellite data. We employed aerosol optical depth (AOD), land surface temperature (LST), normalized difference vegetation index (NDVI), and elevation (EV) in our analysis. The approach incorporates a weighted sum contrast log-linear regression model that integrates satellite data, allowing for the examination of small-scale hourly variations in PM2.5 concentrations. The results reveal a high correlation between hourly PM2.5 levels and AOD, LST, NDVI, EV, time, and week of the year in terms of spatial distribution, with an R2 value of 53.8%. The mean hourly PM2.5 concentration was 23.1 μg/m3, displaying elevated concentrations during the dry season (November to March) and peak hours (8 to 11 a.m. and 8 to 12 p.m.). Positive correlations between AOD and PM2.5, especially when AOD exceeded 0.52, and between LST and PM2.5, particularly when LST exceeded 33.9 °C, along with NDVI ranging from −0.08 to 0.18 and EV above 67.9 m, resulted in higher PM2.5 levels than the overall mean. The proposed model proved valuable for interpretation and practical application, offering comparable estimated hourly PM2.5 concentrations at a 1-km resolution with monitoring stations. This suggests that researchers or policymakers may use the model to understand hourly PM2.5 fluctuations and their impact on human health and the environment.

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.

Objective: MicroRNA (miRNA), a short noncoding RNA, is claimed to be a potential blood-based biomarker. We aimed to identify and evaluate miRNAs as diagnostic biomarkers for non-small cell lung cancer (NSCLC). Methods: Profiles of 745 miRNAs were screened in the serum of 8 patients with NSCLC and 8 age- and sex-matched controls using TaqMan low-density arrays (TLDAs) and validated in 25 patients with NSCLC and 30 with other lung diseases (OLs) as well as in 19 healthy persons (HPs). The diagnostic performance of the candidate miRNAs was assessed in 117 cases of NSCLC and 113 OLs using quantitative real-time polymerase chain reaction (qRT-PCR). Differences in miRNA expression between patients with NSCLC and controls were assessed using the Mann–Whitney U test. The area under receiver operating characteristic (ROC) curve (AUC) was obtained based on the logistic regression model. Results: Ten miRNAs were found to be differentially expressed between patients with NSCLC and controls, including miR-769, miR-339-3p, miR-339-5p, miR-519a, miR-1238, miR-99a#, miR-134, miR-604, miR-539, and miR-342. The expression of miR-339-3p was significantly higher in patients with NSCLC than in those with OLs (P < 0.001) and HPs (P = 0.020). ROC analysis revealed an miR-339-3p expression AUC of 0.616 [95% confidence interval (CI): 0.561–0.702]. The diagnostic prediction was increased (AUC = 0.706, 95% CI: 0.649–0.779) in the model combining miR-339-3p expression and other known risk factors (i.e., age, smoking status, and drinking status). Conclusions: MiR-339-3p was significantly upregulated in patients with NSCLC compared with participants without cancer, suggesting a diagnostic prediction value for high-risk individuals. Therefore, miR-339-3p expression could be a potential blood-based biomarker for NSCLC.