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.

Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO2 emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country.

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.

Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R2: 0.388-0.484) and had the worst direct (R2: 0.0001-0.349) and indirect transferability (R2: 0.07-0.352). Those models using the traditional method had the highest model fitting R2 (0.629-0.966) with lower cross-validation R2 (0.495-0.941) and slightly better direct transferability (R2: 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R2 (0.601-0.966) and the best cross-validation R2 (0.514-0.941). They also had the strongest direct transferability (R2: 0.006-0.590) and moderate-to-good indirect transferability (R2: 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.

Background: Previous studies suggested that exposure to air pollution could increase risk of asthma attacks in children. The aim of this study is to investigate the short-term effects of exposure to ambient air pollution on asthma hospital admissions in children in Beijing, a city with serious air pollution and high-quality medical care at the same time. Methods: We collected hospital admission data of asthma patients aged ≤ 18 years old from 56 hospitals from 2013 to 2016 in Beijing, China. Time-stratified case-crossover design and conditional Poisson regression were applied to explore the association between risk of asthma admission in children and the daily concentration of six air pollutants [particulate matter ≤ 2.5 μm (PM2.5), particulate matter ≤ 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)], adjusting for meteorological factors and other pollutants. Additionally, stratified analyses were performed by age, gender, and season. Results: In the single-pollutant models, higher levels of PM2.5, SO2, and NO2 were significantly associated with increased risk of hospital admission for asthma in children. The strongest effect was observed in NO2 at lag06 (RR = 1.25, 95%CI: 1.06-1.48), followed by SO2 at lag05 (RR = 1.17, 95%CI: 1.05-1.31). The robustness of effects of SO2 and NO2 were shown in two-pollutant models. Stratified analyses further indicated that pre-school children (aged ≤ 6 years) were more susceptible to SO2. The effects of SO2 were stronger in the cold season, while the effects of NO2 were stronger in the warm season. No significant sex-specific differences were observed. Conclusions: These results suggested that high levels of air pollution had an adverse effect on childhood asthma, even in a region with high-quality healthcare. Therefore, it will be significant to decrease hospital admissions for asthma in children by controlling air pollution emission and avoiding exposure to air pollution.

This study reported a novel humidity-insensitive nitrogen dioxide (NO2) gas sensor based on tin dioxide (SnO2)/reduced graphene oxide (rGO) composites through the sol-gel method. The sensor demonstrated ppb-level NO2 detection in p-type sensing behaviors (13.6% response to 750 ppb). Because of the synergistic effect on SnO2/rGO p-n heterojunction, the sensing performance was greatly enhanced compared to that of bare rGO. The limit of detection of sensors was as low as 6.7 ppb under dry air. Moreover, benefited from the formed superhydrophobic structure of the SnO2/rGO composites (contact angle: 149.0°), the humidity showed a negligible influence on the dynamic response (Sg) of the sensor to different concentration of NO2 when increasing the relative humidity (RH) from 0 to 70% at 116°C. The relative conductivity of the sensor to 83% relative humidity was 0.11%. In addition, the response ratio (Sg/SRH) between 750 ppb NO2 and 83% RH was 649.0, indicating the negligible impaction of high-level ambient humidity on the sensor. The as-fabricated humidity-insensitive gas sensor can promise NO2 detection in real-world applications such as safety alarm, chemical engineering, and so on.

School-age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children\'s exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real-time PM10 , PM2.5 , NO2, and O3 concentrations were measured in 76 classrooms and 23 non-classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM2.5 , PM10 , O3, and NO2 microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM2.5 , PM10 , O3, and NO2 microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.

Formation of the semiconductor/dielectric double-layered films via vertical phase separations from polymer blends is an effective method to fabricate organic thin-film transistors (OTFTs). Here, we introduce a simple one-step processing method for the vertical phase separation of poly(3-hexylthiophene-2,5-diyl) (P3HT) and poly(methyl methacrylate) (PMMA) blends in OTFTs and their applications for high-performance nitrogen dioxide (NO2) sensors. Compared to the conventional two-step coated OTFT sensors, one-step processed devices exhibit a great enhancement of the responsivity from 116 to 1481% for 30 ppm NO2 concentration and a limit of detection of ∼0.7 ppb. Studies of the microstructures of the blend films and the electrical properties of the sensors reveal that the devices formed by the one-step vertical phase separation have better capability for the adsorption of NO2 molecules. Moreover, a careful adjustment of the blend ratio between P3HT and PMMA can further improve the performance of the NO2 sensors, ranging from sensitivity to selectivity and to the ability of recovery. This simple one-step processing method demonstrates a potential possibility for developing high-performance, low-cost, and large-area OTFT gas sensors.

The aim of this study was to investigate whether ambient nitrogen dioxide (NO2) and carbon monoxide (CO) increase the risk for age-related macular degeneration (AMD). This is a longitudinal population-based study using the data on Taiwan National Health Insurance Program between year 2000 and 2010. From the nationwide dataset, we enrolled subjects aged 50 or older and the annually total NO2 and CO exposure was calculated from 1998 to 2010 for each subject. The Cox proportional hazard regression was used to estimate the HRs with adjustment for other variables. A total of 39,819 AMD-free residents were enrolled, and 1442 participants developed AMD during the 11 -year follow-up. Compared with the lowest exposure quartile, the highest quartile of each air pollutant was associated with an increased risk for AMD. The adjusted HR was 1.91 (95% CI 1.64 to 2.23, p<0.001) for the highest NO2 quartile, and was 1.84 (95% CI 1.5 to 2.15, p<0.001) for the highest CO quartile. In this study, chronic exposure to the highest quartile of ambient NO2 or CO significantly increases the risk for AMD.