Previous studies showed the adverse impacts of air pollution on headache attacks in developed countries. However, evidence is limited to the impact of exposure to air pollutants on headache attacks. In this study, we aimed to explore the impact of nitrogen dioxide (NO2) exposure on neurology clinic visits (NCVs) for headache onsets.
Records of NCVs for headaches, concentrations of ambient NO2, and meteorological variables were collected in Wuhan, China, from January 1st, 2017, to November 30th, 2019. A time-series study was conducted to investigate the short-term effects of NO2 exposure on daily NCVs for headaches. Stratified analyses were also computed according to season, age, and sex, and the exposure-response (E-R) curve was then plotted.
A total of 11,436 records of NCVs for headaches were enrolled in our study during the period. A 10-μg/m3 increase of ambient NO2 corresponded to a 3.64% elevation of daily NCVs for headaches (95%CI: 1.02%, 6.32%, P = 0.006). Moreover, females aged less than 50 years of age were more susceptible compared to males (4.10% vs. 2.97%, P = 0.007). The short-term effects of NO2 exposure on daily NCVs for headaches were stronger in cool seasons than in warm seasons (6.31% vs. 0.79%, P = 0.0009).
Our findings highlight that short-term exposure to ambient NO2 positively correlated with NCVs for headaches in Wuhan, China, and the adverse effects varied by season, age, and sex.

Air pollution is one of the most alarming environmental issues which causes multiple health hazards. An association between air pollution and cardiovascular diseases has been established through many prior studies. In this study, we aimed to evaluate the risk of long-term exposure to air pollution (PM2.5, CO, and NO2) and its association with the risk of developing peripheral arterial occlusive disease (PAOD). PAOD is a condition involving impairment of perfusion of blood in the distal parts of the aorta due to narrowing of the arteries (arterial stenosis) and has been reported as a risk factor for developing cardiovascular diseases. Furthermore, the risk of PAOD increases with age, and hence is a serious public health issue and a cause for concern, especially for an aging society such as Taiwan. Two national-scale databases from Taiwan, the national health insurance database (NHIRD) and the Taiwan air quality-monitoring database (TAQMD), were linked to conduct this cohort study between 2003 and 2013. Cox proportional hazards regression with time-dependent modeling was used to evaluate the hazard ratio (HR) for PAOD with respect to daily exposure to air pollutants. The concentrations of each of the pollutants of interest (PM2.5, NO2, and CO) were categorized into four categories according to the daily average concentration of air pollutants for every quarter of the year, Q1 to Q4 (Q4 = highest). The cumulative incidence of PAOD was examined by Kaplan-Meier analysis with two-tailed log-rank test. A total of 1,598 PAOD cases were identified during the 10-year follow-up period, along with 98,540 non-PAOD controls. In the multivariate analysis, after adjusting for age, gender, urbanization level, residential area, baseline comorbidities, and medications, the adjusted HRs were PM2.5 = 1.14 (95% CI 1.13-1.16), NO2 = 1.03 (95% CI 1.02-1.04), and CO = 2.35 (95% CI 1.95-2.84). Kaplan-Meier analysis showed that CO (P < 0.0001) and PM2.5 (P < 0.0001) concentrations were strongly and positively associated with the cumulative incidence of PAOD during the follow-up period. Findings from this study established that prolonged exposure to air pollutants CO and PM2.5 are significant factors that, among other well-known causes, may also play a potential role in PAOD pathogenesis.

Air pollutants as risk factors for benign brain tumor (BBT) remain unclear. Therefore, we conducted a nationwide retrospective cohort study by integrating the patients\' clinical data and daily air quality data to assess the environmental risk factors of BBT in Taiwan.Daily air quality data were categorized into quartiles (Q1 to Q4). The adjusted hazard ratio (aHR) was evaluated by comparing the BBT incidence rate of the subjects in Q2-Q4 with that of the subjects in Q1 (the lowest concentration of air pollutants). A total of 161,213 subjects were enrolled in the study. Among the air pollutants tested, the aHR of BBT was significantly higher in the subjects who were exposed to the highest level (Q4) of CO (aHR 1.37, 95% CI 1.08-1.74), NO2 (aHR 1.40, 95% CI 1.09-1.78), and PM2.5 (aHR 1.30, 95% CI 1.02-1.65) than that in the subjects who were exposed to the lowest level (Q1). No significant risk association of BBT with SO2 and PM10 exposure was observed. The results revealed that long-term exposure to air pollutants, particularly CO, NO2, and PM2.5, is associated with the risk of BBT.

The COVID-19 pandemic has significantly affected economic activities all around the world. Though it took a huge amount of human breathes as well as increases unemployment, it puts a positive impression on the environment. To stop the speedy extend of this disease, the maximum Government has imposed a strict lockdown on their citizens which creates a constructive impact on the atmosphere. Air pollutant concentration has been investigated in this study to analyze the impact of lockdown on the environment. Based on the air pollutant concentration, Air Quality Index (AQI) is deliberated. The Air Quality Index indicates the most and least polluted cities in the world. A higher value of AQI represents the higher polluted city and a lesser value of Air Quality Index represents a less polluted city. The impact of lockdown on air quality has been studied in this work and it is observed that the air pollutant concentration has reduced in every city of the world during the lockdown period. It has been also detected that the PM2.5 and PM10 are the most affecting air concentrator which controls the air quality of all the selected places during and after lockdown.

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49-0.50 for PM10 and 0.46-0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.