Markov chain modeling for air pollution index based on maximum a posteriori method Abstract Air pollution is a major environmental problem, which brings about a threat to human health and the natural environment. Thus, determination and assessment of the level of air pollution is an important component in monitoring of the air quality. This study involves estimating the transition probability matrix of the Markov chain model based on maximum a posteriori (MAP) method using hourly data of air pollution index (API). The API data has been collected from seven air-monitoring stations in peninsular Malaysia. The estimated transition probability matrix is used to determine the characteristics of air pollution such as the steady-state probability and the expected return time. The transition probabilities of the Markov chain are fitted based on maximum a posteriori method under three different priors, which are Dirichlet, Jeffreys, and uniform. The results found show that the maximum a posteriori method under the Dirichlet prior produced the most precise estimates as compared with the other priors. In addition, for the areas considered in the study, the moderate state is more persistent as opposed to unhealthy states indicating that the problem of air quality is not very serious. In general, this study could provide important implications for developing proper strategies for managing air quality and for improving public health.

Exposures and effects from fragranced consumer products in Germany Abstract Fragranced consumer products—such as cleaning supplies, laundry products, perfumes, and air fresheners—have been associated with adverse human health effects and subsequent impacts in society. This study investigates effects associated with exposures to fragranced consumer products in Germany. Using a nationally representative population-based sample (n = 1102), data were collected in March 2019 using an online survey of adults in Germany. The study found that, across the German population, 19.9% report health problems, such as respiratory problems (55.3%), migraine headaches (25.1%), and asthma attacks (16.9%), when exposed to fragranced products. Of these reports of health effects, 33.8% could be considered potentially disabling. Further, 5.5% of the population have lost workdays or a job, in the past year, due to exposure to fragranced products in the workplace. A majority of Germans would prefer that workplaces, health care facilities and professionals, airplanes, and hotels were fragrance-free rather than fragranced. Results from this study provide new evidence that exposures to fragranced consumer products are associated with adverse health and societal effects among the German population, and that reducing exposures such as through fragrance-free policies could provide benefits.

Using a low-cost monitor to assess the impact of leaf blowers on particle pollution during street cleaning Abstract Personal exposure to particulate matter (PM) is associated with a variety of adverse health effects and cardiopulmonary diseases. As a mitigating measure to improve air quality, policymakers should select street cleaning tools according to their potential environmental impact, but there is little information about their actual effect on particle pollution. This paper describes the contribution made by leaf blowers to suspended PM and analyzes the duration of this effect during street sweeping in an urban environment. Particle concentration has been monitored throughout a fixed-site 104-day sampling campaign using the Dylos DC1700, a low-cost real-time laser particle counter. This detector recognizes two sizes of particles, coarse and fine, and records data every minute, which provides unique time resolution in the observation of the effect of leaf blowers. The results show that the use of leaf blowers raises fine PM to 13.9 μg/m3 and coarse ones to 31.5 μg/m3, which increases the number 1.6 times and 1.7 times, respectively, when compared with normal median particle concentration. The particulate matter stays resuspended in the air for several minutes, creating a dust wave effect. Low-cost sensors, such as the Dylos, are proposed as a practical methodology to help local decision-makers incorporate environmental variables in decision-making.

Optical aerosol properties of megacities: inland and coastal cities comparison Abstract Measurements of aerosol optical depths allow the determination of microphysical and radiative characteristics of atmospheric aerosols and specially that of Megacities, which contribute to the deterioration of air quality live, increase health effects, and anthropogenic climate change. This paper analyzes the aerosol optical properties of ten megacities classified on inland and costal sites. The annual average of aerosol optical depths are around 0.5 and peaks can exceed 4 especially in summer for East Asia (Beijing and Bangkok) where the involvement of the anthropogenic aerosol is more important. Single scattering albedo is often greater than 0.8 and sometimes show wide variations between 0.6 and 0.98. The refractive index is constant and stands at 1.47 for the real part and 16 10−3 for its imaginary part. The PSDs are 0.16 μm for the fine mode and 2.3 μm for the coarse particle mode with a 3 μm magnification trend for the coastal sites. The volume concentrations are on average close to 0.1 μm3/μm2 for large particles and 0.04 μm3/μm2 for fines with peaks observed at Ilorin for large and at Beijing for fines. Radiative forcing are always negative (cooling trends), relatively low at the top of the atmosphere, larger at surface, and relatively higher at coastal sites. For the vertical atmospheric column, anthropogenic radiative forcing is always positive (warming trends) estimated average of + 14 W/m2 and natural registers three times increase for coastal sites. In reality, the coastal distinction is not at the origin of this increase since the maxima recorded are also included in the inland sites (Riyadh and Ilorin).

Predictive modeling of PM 2.5 using soft computing techniques: case study—Faridabad, Haryana, India Abstract Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and largest city of Haryana, India, and the current study was designed to foresee the PM2.5 content by different modeling techniques: (1) support vector machine (SVM), (ii) random forest (RF), (iii) artificial neural network (ANN), (iv) M5P model, and (v) Gaussian process regression (GP). Collected data (659 observations) from May 2015 to May 2018 were used to develop the models. Parameters such as temperature (T), ground-level ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), NOx, wind speed (WS), wind direction (WD), relative humidity (RH), bar pressure (BP), and solar radiation (SR) are used as input parameters for prediction of PM2.5. The results of all the models suggested that RF model with testing correlation coefficient (CC) 0.8312, mean absolute error (MAE) 30.7757, R2 (correlation of determination) 0.6909, and root mean square error (RMSE) 44.6947 is the best estimator for appraisal of PM2.5 followed by SVM, GP, M5P, and ANN models. The sensitivity analysis results indicated that wind speed is the utmost influencing parameter for the estimation of PM2.5. Abilities of different models were compared and RF was established as the best technique based on assessment criteria. We recommend more studies employing RF and other techniques as hybrids that lead to better models.

Environmental factors and asthma hospitalization in Montreal, Canada, during spring 2006–2008: a synergy perspective Abstract The aim of this study is to analyze the synergy between environmental factors (pollutant, allergenic birch pollen, weather) and its relationship with asthma hospitalization in Montreal, Canada. The data is stratified into weather types and the study restricted to spring season to limit the impact of multiple confounders. Results shows that the daily count of asthma hospitalization (spring 2006–2008) in situation of warm fronts or trowals (daily average of 3.78 counts, CI 95% 2.95–4.61) was much higher (p < 0.001) than in other situations (2.49 counts, CI 95% 2.37–2.71). Moreover, the explained variance of asthma hospitalization due to air pollution rises from about less than 7% (in the case of no stratification) to about 28% (R = 0.53, p < 0.05 with stratification). Statistical tests for interaction and overall results point towards a synergy between environmental factors which exacerbates asthma. A new concept named frontal asthma is proposed to explain several results found here and in the open literature.

Concentrations, enrichment, and sources of metals in PM 2.5 in Beijing during winter Abstract Serious haze-fog problems in Beijing have aroused considerable concern. The objectives of this study are to determine elemental concentrations in PM2.5, assess their enrichment in PM2.5, and identify their sources during winter in Beijing. Daily PM2.5 samples were collected at the Beijing Normal University campus from January 28 to February 27, 2014. Twenty-three metal elements were determined following digestion by using inductively coupled-plasma mass spectrometry (ICP-MS). The daily PM2.5 concentration ranged from 21.8 to 474.3 μg m−3, with a mean of 172 μg m−3. The daily PM2.5 concentration exceeded 75 mg m−3 (China ambient air quality standards: grade II) for 23 days of the month; therefore, ambient air pollution was severe. The median of daily elemental concentrations in the air decreased in the order of K, Na, and Al (> 103 ng m−3); Ca, Fe, Mn, Mg, Zn, Pb, and Cu (> 102 ng m−3); Ba, P, Ti, and Sr (> 10 ng m−3); and Rb, Sb, Ga, V, Ce, Cd, Tl, Li, La, and Co (> 1 ng m−3). Average and median values of enrichment factors were > 100 for Cd, Sb, Pb, Cu, Zn, and Tl but < 10 for Sr, V, K, Co, Na, Li, Rb, La, Mg, Ce, Ca, Fe, and Al. Enrichment factor, correlation, and principal component analyses demonstrated that Cd, Sb, Pb, Cu, Zn, and Tl in PM2.5 mainly originated from anthropogenic sources, whereas V, Co, Na, Li, Rb, La, Ce, Fe, and Al mainly originated from lithogenic sources. However, the ignition of fireworks during Spring Festival resulted in three pronounced peaks of Ba, K, Mg, Sr, and Cu concentrations. Therefore, national and local governments should continue controlling atmospheric emissions of toxic heavy metals and prohibit firework displays in megacities during Spring Festival.

Monthly variation in masses, metals and endotoxin content as well as pro-inflammatory response of airborne particles collected by TEOM monitors Abstract Particle exposure has been linked to an increased incidence of cardiovascular disease. Furthermore, particle exposure has been shown to have a chronic inhibitory effect on lung development in young people and may result in increased respiratory problems in adults or children with respiratory-related diseases. In today’s urban environments, particle levels are mainly monitored gravimetrically; however, other factors such as particle size, shape and surface reactivity have recently been noted as highly important in relation to possible health outcomes. Here, particles from TEOM monitor filters placed in three different cities were studied. The purpose of the study was to investigate whether there are variations in particle masses, cadmium and lead contents, as well as endotoxin levels between locations and time points over the year and if this can be correlated to the particles ability to induce a pro-inflammatory response in vitro. Results showed that it is possible to detect variations at different locations and at different time points over the year and that cadmium, lead and endotoxin levels did not coincide with the increased total particle masses while endotoxin levels coincided with pro-inflammatory responses in vitro. The present study shows that filter analysis is a useful complement to gravimetric or particle-counting measurements in studies of particle-related health effects and will give useful information regarding future air quality measurements.

Active botanical biofiltration of air pollutants using Australian native plants Abstract Air pollutants are of public concern due to their adverse health effects. Biological air filters have shown great promise for the bioremediation of air pollutants. Different plant species have previously been shown to significantly influence pollutant removal capacities, although the number of species tested to date is small. The aims of this paper were to determine the pollutant removal capacity of different Australian native species for their effect on active biowall particulate matter, volatile organic compounds and carbon dioxide removal, and to compare removal rates with previously tested ornamental species. The single-pass removal efficiency for PM and VOCs of native planted biofilters was determined with a flow-through chamber. CO2 removal was tested by a static chamber pull down study. The results indicated that the native species were not effective for CO2 removal likely due to their high light level requirements in conjunction with substrate respiration. Additionally, the native species had lower PM removal efficiencies compared to ornamental species, with this potentially being due to the ornamental species possessing advantageous leaf traits for increased PM accumulation. Lastly, the native species were found to have similar benzene removal efficiencies to ornamental species. As such, whilst the native species showed a capacity to phytoremediate air pollutants, ornamental species have a comparatively greater capacity to do so and are more appropriate for air filtration purposes in indoor circumstances. However, as Australian native plants have structural and metabolic adaptations that enhance their ability to tolerate harsh environments, they may find use in botanical biofilters in situations where common ornamental plants may be suitable, especially in the outdoor environment.

Stack emissions and health risk integrated (SEHRI) model: a tool for stack emissions and health risk modeling Abstract The quantitative assessment of health burden of coal power stack emissions is a vital step to frame effective policy measures to achieve emission reductions and corresponding health benefits. A plant-specific stack emission, dispersion, and health risk integrated (SEHRI) model has been proposed to estimate coal power stack emissions, resulting ambient air concentrations and associated health risks in the vicinity of a coal power plant. In this study, we have estimated the health benefits due to closure of Badarpur Thermal Power Station (BTPS), New Delhi. The SEHRI model has been applied to estimate decreased health risks due to particulates (PM2.5) and gaseous (SO2, NOx) stack emissions released from the plant. More than 1050 premature deaths are avoided and 526 life years are saved resulting from reduction in particulate and gaseous stack emissions, respectively, due to closure of the plant. It is expected that environmental engineers, scientists, and air quality managers would find the SEHRI model as a useful tool to study emissions and health impacts related to coal-based power plants and guide an effective control policy for air pollution abatement.