Previous studies have suggested that short-term exposure to ambient air pollution was associated with pediatric hospital admissions and emergency room visits for certain respiratory diseases; however, there is limited evidence on the association between short-term air pollution exposure and pediatric outpatient visits. Our aim was to quantitatively assess the short-term effects of ambient air pollution on pediatric outpatient visits for respiratory diseases. We conducted a time-series study in Yichang city, China between Jan 1, 2014 and Dec 31, 2015. Daily counts of pediatric respiratory outpatient visits were collected from 3 large hospitals, and then linked with air pollution data from 5 air quality monitoring stations by date. We used generalized additive Poisson models to conduct linear and nonlinear exposure-response analyses between air pollutant exposures and pediatric respiratory outpatient visits, adjusting for seasonality, day of week, public holidays, temperature, and relative humidity. Each interquartile range (IQR) increase in PM2.5 (lag 0), PM10 (lag 0), NO2 (lag 0), CO (lag 0), and O3 (lag 4) concentrations was significantly associated with a 1.91% (95% CI: 0.60%, 3.23%), 2.46% (1.09%, 3.85%), 1.88% (0.49%, 3.29%), 2.00% (0.43%, 3.59%), and 1.91% (0.45%, 3.39%) increase of pediatric respiratory outpatient visits, respectively. Similarly, the nonlinear exposure-response analyses showed monotonic increases of pediatric respiratory outpatient visits by increasing air pollutant exposures, though the associations for NO2 and CO attenuated at higher concentrations. These associations were unlikely modified by season. We did not observe significant association for SO2 exposure. Our results suggest that short-term exposures to PM2.5, PM10, NO2, CO, and O3 may account for increased risk of pediatric outpatient visits for respiratory diseases, and emphasize the needs for reduction of air pollutant exposures for children.

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management.

Air pollution plays a pivotal role in the deterioration of many materials used in buildings and cultural monuments causing an inestimable damage. This study aims to estimate the impacts of air pollution (SO2, HNO3, O3, PM10) and meteorological conditions (temperature, precipitation, relative humidity) on limestone, copper and bronze based on high resolution air quality data-base produced with AMS-MINNI modelling system over the Italian territory over the time period 2003–2010. A comparison between high resolution data (AMS-MINNI grid, 4 × 4 km) and low resolution data (EMEP grid, 50 × 50 km) has been performed. Our results pointed out that the corrosion levels for limestone, copper and bronze are decreased in Italy from 2003 to 2010 in relation to decrease of pollutant concentrations. However, some problem related to air pollution persists especially in Northern and Southern Italy. In particular, PM10 and HNO3 are considered the main responsible for limestone corrosion. Moreover, the high resolution data (AMS-MINNI) allowed the identification of risk areas that are not visible with the low resolution data (EMEP modelling system) in all considered years and, especially, in the limestone case. Consequently, high resolution air quality simulations are suitable to provide concrete benefits in providing information for national effective policy against corrosion risk for cultural heritage, also in the context of climate changes that are affecting strongly Mediterranean basin.

Volatile organic compounds (VOCs) from two sampling sites (HB and XB) in a power station centralized area, in Shuozhou city, China, were sampled by stainless steel canisters and measured by gas chromatography-mass selective detection/flame ionization detection (GC-MSD/FID) in the spring and autumn of 2014. The concentration of VOCs was higher in the autumn (HB, 96.87 μg/m3; XB, 58.94 μg/m3) than in the spring (HB, 41.49 μg/m3; XB, 43.46 μg/m3), as lower wind speed in the autumn could lead to pollutant accumulation, especially at HB, which is a new urban area surrounded by residential areas and a transportation hub. Alkanes were the dominant group at both HB and XB in both sampling periods, but the contribution of aromatic pollutants at HB in the autumn was much higher than that of the other alkanes (11.16–19.55%). Compared to other cities, BTEX pollution in Shuozhou was among the lowest levels in the world. Because of the high levels of aromatic pollutants, the ozone formation potential increased significantly at HB in the autumn. Using the ratio analyses to identify the age of the air masses and analyze the sources, the results showed that the atmospheric VOCs at XB were strongly influenced by the remote sources of coal combustion, while at HB in the spring and autumn were affected by the remote sources of coal combustion and local sources of vehicle emission, respectively. Source analysis conducted using the Positive Matrix Factorization (PMF) model at Shuozhou showed that coal combustion and vehicle emissions made the two largest contributions (29.98% and 21.25%, respectively) to atmospheric VOCs. With further economic restructuring, the influence of vehicle emissions on the air quality should become more significant, indicating that controlling vehicle emissions is key to reducing the air pollution.

Vigorous air pollution control measures were implemented during the 2014 Asia-Pacific Economic Cooperation and a large-scale military parade (described here as “APEC Blue” and “Parade Blue” periods) in Beijing, China. A natural experiment was conducted in a health impact assessment framework to estimate the number of deaths attributable to PM2.5, using concentration-response functions derived from previous studies conducted in Beijing, combined with the differences in PM2.5 concentrations between intervention and reference periods. Substantial reductions in daily PM2.5 concentrations were observed during both intervention periods. Using the same dates from the prior year as a reference, daily PM2.5 concentration decreased from 98.57 μg/m³ to 47.53 μg/m³ during “APEC Blue”, and from 59.15 μg/m³ to 17.07 μg/m³ during the “Parade Blue”. We estimated that 39–63 all-cause deaths (21–51 cardiovascular, 6–13 respiratory deaths) have been prevented during the APEC period; and 41–65 deaths (22–52 cardiovascular, 6–13 respiratory deaths) have been prevented during the Parade period. This study shows that substantial mortality reductions could be achieved by implementing stringent air pollution mitigation measures.

Green streets are increasingly being used as a stormwater management strategy to mitigate stormwater runoff at its source while providing other environmental and societal benefits, including connecting pedestrians to the street. Simultaneously, human exposure to particulate matter from urban transportation is of major concern worldwide due to the proximity of pedestrians, drivers, and cyclists to the emission sources. Vegetation used for stormwater treatment can help designers limit the exposure of people to air pollutants. This goal can be achieved through the deliberate placement of green streets, along with strategic planting schemes that maximize pollutant dispersion. This communication presents general design considerations for green streets that combine stormwater management and air quality goals. There is currently limited guidance on designing green streets for air quality considerations; this is the first communication to offer suggestions and advice for the design of green stormwater streets in regards to their effects on air quality. Street characteristics including (1) the width to height ratio of the street to the buildings, (2) the type of trees and their location, and (3) any prevailing winds can have an impact on pollutant concentrations within the street and along sidewalks. Vegetation within stormwater control measures has the ability to reduce particulate matter concentrations; however, it must be carefully selected and placed within the green street to promote the dispersion of air flow.

This manuscript evaluates spatial and temporal variations of source contributions to ambient fine particulate matter (PM2.5) in Israeli, Jordanian, and Palestinian cities. Twenty-four hour integrated PM2.5 samples were collected every six days over a 1-year period (January to December 2007) in four cities in Israel (West Jerusalem, Eilat, Tel Aviv, and Haifa), four cities in Jordan (Amman, Aqaba, Rahma, and Zarka), and three cities in Palestine (Nablus, East Jerusalem, and Hebron). The PM2.5 samples were analyzed for major chemical components, including organic carbon and elemental carbon, ions, and metals, and the results were used in a positive matrix factorization (PMF) model to estimate source contributions to PM2.5 mass. Nine sources, including secondary sulfate, secondary nitrate, mobile, industrial lead sources, dust, construction dust, biomass burning, fuel oil combustion and sea salt, were identified across the sampling sites. Secondary sulfate was the dominant source, contributing 35% of the total PM2.5 mass, and it showed relatively homogeneous temporal trends of daily source contribution in the study area. Mobile sources were found to be the second greatest contributor to PM2.5 mass in the large metropolitan cities, such as Tel Aviv, Hebron, and West and East Jerusalem. Other sources (i.e. industrial lead sources, construction dust, and fuel oil combustion) were closely related to local emissions within individual cities. This study demonstrates how international cooperation can facilitate air pollution studies that address regional air pollution issues and the incremental differences across cities in a common airshed. It also provides a model to study air pollution in regions with limited air quality monitoring capacity that have persistent and emerging air quality problems, such as Africa, South Asia and Central America.

Low-cost, light-scattering-based particulate matter (PM) sensors are becoming more widely available and are being increasingly deployed in ambient and indoor environments because of their low cost and ability to provide high spatial and temporal resolution PM information. Researchers have begun to evaluate some of these sensors under laboratory and environmental conditions. In this study, a low-cost, particulate matter sensor (Plantower PMS 1003/3003) used by a community air-quality network is evaluated in a controlled wind-tunnel environment and in the ambient environment during several winter-time, cold-pool events that are associated with high ambient levels of PM. In the wind-tunnel, the PMS sensor performance is compared to two research-grade, light-scattering instruments, and in the ambient tests, the sensor performance is compared to two federal equivalent (one tapered element oscillating microbalance and one beta attenuation monitor) and gravimetric federal reference methods (FEMs/FRMs) as well as one research-grade instrument (GRIMM). The PMS sensor response correlates well with research-grade instruments in the wind-tunnel tests, and its response is linear over the concentration range tested (200–850 μg/m³). In the ambient tests, this PM sensor correlates better with gravimetric methods than previous studies with correlation coefficients of 0.88. However additional measurements under a variety of ambient conditions are needed. Although the PMS sensor correlated as well as the research-grade instrument to the FRM/FEMs in ambient conditions, its response varies with particle properties to a much greater degree than the research-grade instrument. In addition, the PMS sensors overestimate ambient PM concentrations and begin to exhibit a non-linear response when PM2.5 concentrations exceed 40 μg/m³. These results have important implications for communicating results from low-cost sensor networks, and they highlight the importance of using an appropriate correction factor for the target environmental conditions if the user wants to compare the results to FEM/FRMs.

Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

High energy electron-impact ionizers have found applications mainly in industry to reduce off-gas emissions from waste gas streams at low cost and high efficiency because of their ability to oxidize many airborne organic pollutants (e.g., volatile organic compounds (VOCs)) to CO2 and H2O. Applications of air ionizers in indoor air quality management are limited due to poor removal efficiency and production of noxious side products, e.g., ozone (O3). In this paper, we provide a critical evaluation of the pollutant removal performance of air ionizing system through comprehensive review of the literature. In particular, we focus on removal of VOCs and odorants. We also discuss the generation of unwanted air ionization byproducts such as O3, NOx, and VOC oxidation intermediates that limit the use of air-ionizers in indoor air quality management.