Air pollution and its health impacts: the changing panorama

Disruption of Atrial Rhythmicity by the Air Pollutant 1,2-Naphthoquinone: Role of Beta-Adrenergic and Sensory Receptors

The combustion of fossil fuels contributes to air pollution (AP), which was linked to about 8.79 million global deaths in 2018, mainly due to respiratory and cardiovascular-related effects. Among these, particulate air pollution (PM2.5) stands out as a major risk factor for heart health, especially during vulnerable phases. Our prior study showed that premature exposure to 1,2-naphthoquinone (1,2-NQ), a chemical found in diesel exhaust particles (DEP), exacerbated asthma in adulthood. Moreover, increased concentration of 1,2-NQ contributed to airway inflammation triggered by PM2.5, employing neurogenic pathways related to the up-regulation of transient receptor potential vanilloid 1 (TRPV1). However, the potential impact of early-life exposure to 1,2-naphthoquinone (1,2-NQ) on atrial fibrillation (AF) has not yet been investigated. This study aims to investigate how inhaling 1,2-NQ in early life affects the autonomic adrenergic system and the role played by TRPV1 in these heart disturbances. C57Bl/6 neonate male mice were exposed to 1,2-NQ (100 nM) or its vehicle at 6, 8, and 10 days of life. Early exposure to 1,2-NQ impairs adrenergic responses in the right atria without markedly affecting cholinergic responses. ECG analysis revealed altered rhythmicity in young mice, suggesting increased sympathetic nervous system activity. Furthermore, 1,2-NQ affected β1-adrenergic receptor agonist-mediated positive chronotropism, which was prevented by metoprolol, a β1 receptor blocker. Capsazepine, a TRPV1 blocker but not a TRPC5 blocker, reversed 1,2-NQ-induced cardiac changes. In conclusion, neonate mice exposure to AP 1,2-NQ results in an elevated risk of developing cardiac adrenergic dysfunction, potentially leading to atrial arrhythmia at a young age.

Multiparametric Sensor Node for Environmental Monitoring Based on Energy Harvesting

The heterogeneity and levels of chemicals released into the environment have dramatically grown in the last few years. Therefore, new low-cost tools are increasingly required to monitor pollution and follow its trends over time. Recent approaches in electronics and wireless communications permit the expansion of low-power, low-cost, and multiparametric sensor nodes that are limited in size and communicate untethered in small distances. For such a monitoring system to be ultimately feasible, a suitable power source for these nodes must be found. The present research falls within the frame of this global effort. The study sits within the context discussed above with the particular aim of developing groundbreaking technology-based solutions by means of efficient environmentally powered wireless smart sensors. This paper presents a multiparametric sensor node for indoor/outdoor air quality monitoring, able to work without battery and human intervention, harvesting energy from the surrounding environment for perpetual operation. The complete system design of the sensor and experimental results are reported. The evaluation of the energy-harvesting blocks with a budget allocation of the power consumption is also discussed.

The impact of the Hazelwood coal mine fire smoke exposure on asthma

OBJECTIVE

In 2014, a fire at an open cut coal mine in South-eastern Australia burned for about 6 weeks. Residents of the adjacent town were exposed to high levels of fine particulate matter (PM2.5) during this period. Three and a half years after the event, this study aimed to investigate potential long-term impacts of short-term exposure to coal mine fire smoke on asthma.

METHODS

A cross-sectional analysis was undertaken in a group of exposed participants with asthma from Morwell (n = 165) and a group of unexposed participants with asthma from the control town of Sale (n = 64). Exposure was determined by modeled PM2.5 data for the mine fire period. Respiratory symptoms were assessed with a validated respiratory health questionnaire and symptom severity score. Asthma control was assessed with a validated questionnaire. Lung function testing included spirometry, bronchodilator response, and fraction of exhaled nitric oxide.

RESULTS

There was no evidence that exposed participants had more severe asthma symptoms, worse lung function, or more eosinophilic airway inflammation than unexposed participants. However, there was some evidence that Morwell participants had more uncontrolled than well-controlled asthma, compared to the participants from Sale (adjusted relative risk ratio 2.71 95% CI: 1.02, 7.21, p = .046).

CONCLUSION

Three and a half years after exposure, coal mine fire smoke did not appear to be associated with more severe asthma symptoms or worse lung function but might be associated with poorer asthma control.

Coal Refinery Process Absorbability Index Assessment against Foot Print of Air Pollution by Usage of Robust Optimization Algorithms: A Novel Green Environment Initiative

Coals are employed as fiery substance, and every day, millions of ton coal are consumed by coal users around the world. It is investigated that the millions of coal’s transportation/logistic till the coal user plants via road route and also inside the coal user plants (known as twice factors) not only enhance the air pollution but also cause the global warming. It is earlier known that coals emit the toxic pollutants and offensive gases such as sulfur dioxide, SO2; nitrogen oxides, NOX; hydrogen chloride arsenic; carbon monoxide, CO; methane; CH4; and CO2 on reacting with environmental O2 due to said twice factors, i.e., during the transportation from coal refinery spot to entry gate of coal user plants (another spot) and in process logistic/movement inside the coal user plants (loading to conveyor to coal fire tubes “attached with coal crushers”). Therefore, the coal refinery technique/process is found as the best practice to control air pollution under concerns of twice factors. The reliable and trustworthy coal refining technology improves the quality of coal by eradicating or eliminating the coating or layers of toxic particles from coal’s surface, which speedily crumble or decompose in reacting with environmental O2 under twice factors. As results, coal refining technology adds the green supply chain value into proposed twice factors and also save the world from breeding of ills and viruses. It is understood that the best coal refinery technique/process helps to overcome and reduce air pollution by responding discussed twice factors (accepted as research challenge and motivation of research). In the presented research work, the authors developed and proposed a dynamic multidimension Coal Refinery Process Absorbability Index (CRPAI) structure (consisted of coal refinery core dimension and subdimensions correspond to CRPA alternative techniques/processes) appended with Robust Optimization Algorithm (ROA) to be explored for opting the best CRP from available options. But due to inherent ambiguity, vagueness, and inconsistency involve in both dimensions of proposed structure, the assessment of expert’s panel is gathered in the terms of linguistic variable “appropriateness ratings” against the subdimensions of CRPAI structure corresponding to preferred CRP options. Next, assigned appropriateness ratings against the subdimensions are substituted by GIVFN. To arrive to core dimensions from subdimensions of CRPAI structure, a GITFN-OWGO (Ordered Weighted Geometric Operator) is investigated and modified as a Ordered Weighted Geometric Average Operator (OWGAO) to be applied for estimating the weights of subdimensions (core novelty of work). Finally, a ROA (consisted of MULTI-MOORA with dominance theory) is applied on the output of OWGAO for opting the viable and best CRP option. The positive effect of the dynamic multidimension CRPAI structure is that it helps the coal refinery companies to assess measure and evaluate the best and feasible coal refinery process under concern of twice factors using expert information. The research can be used to control the air pollution by responding aforesaid twice factors by single practice (the best coal refinery process/technique assessment and evaluation).

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM_2.5; Particulate Matter 10, PM₁₀ and lower atmospheric visibility, VIS), the atmospheric variables associated with increased respiratory induced mortality and recurrent health-care cost. Firstly, hourly air quality data series (January-2015 to December-2017) are demarcated into their respective intrinsic mode functions (IMFs) and a residual sub-series that reveal patterns and resolve data complexity characteristics, followed by partial autocorrelation function applied to each IMF and residual sub-series to unveil historical changes in air quality. To design the prescribed hybrid model, the data is partitioned into training (70%), validation (15%) and testing (15%) sub-sets. The online sequential-extreme learning machine (OS-ELM) algorithm integrated with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is designed as a data pre-processing system to robustly extract predictive patterns and fine-tune the model generalization to a near-optimal global solution, which represents modelled air quality at hourly forecast horizons. The resulting early warning AI-based framework denoted as ICEEMDAN-OS-ELM model, is individually constructed by forecasting each IMF and residual sub-series, with hourly PM_2.5, PM₁₀, and VIS obtained by the aggregated sum of forecasted IMFs and residual sub-series. The results are benchmarked with many competing predictive approaches; e.g., hybrid ICEEMDAN-multiple-linear regression (MLR), ICEEMDAN-M5 model tree and standalone versions: OS-ELM, MLR, M5 model tree. Statistical metrics including the root-mean-square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Legates & McCabe's Index (E_LM) and Nash-Sutcliffe coefficients (E_NS) are used to evaluate the model's accuracy. Both visual and statistical results show that the proposed ICEEMDAN-OS-ELM model registers superior results, outperforming alternative comparison approaches. For instance, for PM_2.5,E_LM values ranged from 0.65-0.82 vs. 0.59-0.77 for ICEEMDAN-M5 tree, 0.59-0.74 for ICEEMDAN-MLR, 0.28-0.54 for OS-ELM, 0.27-0.54 for M5 tree and 0.25-0.53 for the MLR model. For remaining air quality variables (i.e., PM₁₀ & VIS), the objective model (ICEEMDAN-OS-ELM) outperformed the comparative models. In particular, ICEEMDAN-OS-ELM registered relatively low RMSE/MAE, ranging from approximately 0.7-1.03 μg/m³(MAE), 1.01-1.47 μg/m³(RMSE) for PM_2.5 whereas for PM₁₀, these metrics registered a value of 1.29-3.84 μg/m³(MAE), 3.01-7.04 μg/m³(RMSE) and for Visibility, they were 0.01-3.72 μg/m³ (MAE (Mm^-1)), 0.04-5.98 μg/m³ (RMSE (Mm^-1)). Visual analysis of forecasted and observed air quality through a Taylor diagram illustrates the objective model's preciseness, confirming the versatility of early warning AI-model in generating air quality forecasts. The excellent performance ascertains the hybrid model's potential utility for air quality monitoring and subsequent public health risk mitigation.

Synthesis and Electrospraying of Nanoscale MOF (Metal Organic Framework) for High-Performance CO2 Adsorption Membrane

We report the sonochemical synthesis of MOF (metal organic framework) nanoparticles of 30-200 nm in size and electrospraying of those particles on electrospun nanofibers to process a MOF-attached nanofibrous membrane. This membrane displayed significant selectivity towards CO₂ and capacity of adsorbing with 4000-5000 ppm difference from a mixed gas flow of 1% CO₂ and 99% N₂. Applying ultrasonic waves during the MOF synthesis offered rapid dispersion and formation of crystalline MOF nanoparticles in room temperature. The MOF nanoparticles of 100-200 nm in size displayed higher surface area and adsorption capacity comparing to that of 30-60 nm in size. Nanofibrous membrane was produced by electrospinning of MOF blended PAN solution followed by electrospraying of additional MOF nanoparticles. This yielded uniform MOF deposition on nanofibers, occurred due to electrostatic attraction between highly charged nanoparticles and conductive nanofibers. A test bench for real-time CO₂ adsorption at room temperature was built with non-dispersive Infrared (NDIR) CO₂ sensors. Comparative tests were performed on the membrane to investigate its enhanced adsorption capacity. Three layers of the as-produced membranes displayed CO₂ adsorption for approximately 2 h. Thermogravimetric analysis (TGA) of the membrane showed the thermal stability of the MOF and PAN up to 290 and 425 °C, respectively.

Chemical toxicology of reactive species in the atmosphere: two decades of progress in an electron acceptor and an electrophile

Air pollutants such as diesel exhaust particles (DEP) are thought to cause pulmonary diseases such as asthma as a result of oxidative stress. While DEP contain a large number of polycyclic aromatic hydrocarbons, we have focused on 9,10-phenanthrenequinone (9,10-PQ) and 1,2-naphthoquinone (1,2-NQ) because of their chemical properties based on their oxidative and chemical modification capabilities. We have found that 9,10-PQ interacts with electron donors such as NADPH (in the presence of enzymes) and dithiols, resulting in generation of excess reactive oxygen species (ROS) through redox cycling. We have also shown that 1,2-NQ is able to modify protein thiols, leading to protein adducts associated with activation of redox signal transduction pathways at lower concentrations and toxicity at higher concentrations. In this review, we briefly introduce our findings from the last two decades.

The effects of air pollution on asthma hospital admissions in Adelaide, South Australia, 2003-2013: time-series and case-crossover analyses

BACKGROUND

Air pollution can have adverse health effects on asthma sufferers, but the effects vary with geographic, environmental and population characteristics. There has been no long time-series study in Australia to quantify the effects of environmental factors including pollen on asthma hospitalizations.

OBJECTIVES

This study aimed to assess the seasonal impact of air pollutants and aeroallergens on the risk of asthma hospital admissions for adults and children in Adelaide, South Australia.

METHODS

Data on hospital admissions, meteorological conditions, air quality and pollen counts for the period 2003-2013 were sourced. Time-series analysis and case-crossover analysis were used to assess the short-term effects of air pollution on asthma hospitalizations. For the time-series analysis, generalized log-linear quasi-Poisson and negative binomial regressions were used to assess the relationships, controlling for seasonality and long-term trends using flexible spline functions. For the case-crossover analysis, conditional logistic regression was used to compute the effect estimates with time-stratified referent selection strategies.

RESULTS

A total of 36,024 asthma admissions were considered. Findings indicated that the largest effects on asthma admissions related to PM_2.5 , NO₂ , PM₁₀ and pollen were found in the cool season for children (0-17 years), with the 5-day cumulative effects of 30.2% (95% CI: 13.4-49.6%), 12.5% (95% CI: 6.6-18.7%), 8.3% (95% CI: 2.5-14.4%) and 4.2% (95% CI: 2.2-6.1%) increases in risk of asthma hospital admissions per 10 unit increments, respectively. The largest effect for ozone was found in the warm season for children with the 5-day cumulative effect of an 11.7% (95% CI: 5.8-17.9%) increase in risk of asthma hospital admissions per 10 ppb increment in ozone level.

CONCLUSION

Findings suggest that children are more vulnerable and the associations between exposure to air pollutants and asthma hospitalizations tended to be stronger in the cool season compared to the warm season, with the exception of ozone. This study has important public health implications and provides valuable evidence for the development of policies for asthma management.

Air Pollution modifies the association between successful and pathological aging throughout the frailty condition

The rapid growth in the number of older adults has many implications for public health, including the need to better understand the risks posed by environmental exposures. Aging leads to a decline and deterioration of functional properties at the cellular, tissue and organ level. This loss of functional properties yields to a loss of homeostasis and decreased adaptability to internal and external stress. Frailty is a geriatric syndrome characterized by weakness, weight loss, and low activity that is associated with adverse health outcomes. Frailty manifests as an age-related, biological vulnerability to stressors and decreased physiological reserves. Ambient air pollution exposure affects human health, and elderly people appear to be particularly susceptible to its adverse effects. The aim of this paper is to discuss the role of air pollution in the modulation of several biological mechanisms involved in aging. Evidence is presented on how air pollution can modify the bidirectional association between successful and pathological aging throughout the frailty conditions.

Ambient and household air pollution: complex triggers of disease

Concentrations of outdoor air pollution are on the rise, particularly due to rapid urbanization worldwide. Alternatively, poor ventilation, cigarette smoke, and other toxic chemicals contribute to rising concentrations of indoor air pollution. The World Health Organization recently reported that deaths attributable to indoor and outdoor air pollutant exposure are more than double what was originally documented. Epidemiological, clinical, and animal data have demonstrated a clear connection between rising concentrations of air pollution (both indoor and outdoor) and a host of adverse health effects. During the past five years, animal, clinical, and epidemiological studies have explored the adverse health effects associated with exposure to both indoor and outdoor air pollutants throughout the various stages of life. This review provides a summary of the detrimental effects of air pollution through examination of current animal, clinical, and epidemiological studies and exposure during three different periods: maternal (in utero), early life, and adulthood. Additionally, we recommend future lines of research while suggesting conceivable strategies to curb exposure to indoor and outdoor air pollutants.

Particle size distribution of ambient aerosols in an industrial area

Aerosol samples of PM(10) and PM(2.5) were collected from 38 sampling locations in and around the industrial area. The 24 h average mass concentration of PM(10) and PM(2.5) was 137.5 and 61.5 μg/m(3) respectively during summer, 122 and 97.5 μg/m(3) respectively in winter and 70 and 54 μg/m(3) respectively during post monsoon season. The relative contribution of coarse, fine and ultrafine particle to ambient air was analyzed for its temporal and seasonal variability in an industrialized area. This paper aims to establish baseline between PM(10) and PM(2.5) mass concentration levels.

The mining and burning of coal: effects on health and the environment

Australia's coal conundrum is that all political parties say they are concerned about climate change while sanctioning an unprecedented expansion of coalmining and coal seam gas extraction in Australia. Australia's coal contributes to climate change and its global health impacts. Each phase of coal's lifecycle (mining, disposal of contaminated water and tailings, transportation, washing, combustion, and disposing of postcombustion wastes) produces pollutants that affect human health. Communities in which coalmining or burning occurs have been shown to suffer significant health impacts. The health and climate costs of coal are unseen, and when costs to health systems are included, coal is an expensive fuel.

Air quality and chronic disease: why action on climate change is also good for health

There is increasing evidence that air pollution contributes to the burden of chronic disease and premature mortality, particularly from cardiovascular and respiratory causes. Action now urgently required to mitigate climate change has the potential co-benefit of improving air quality and reducing the chronic disease burden. Fossil fuel combustion, primarily from motor vehicles and energy generation, is a major contributor to anthropogenic climate change and air pollution-related health conditions. Action to reduce greenhouse gas emissions by improving energy efficiency, departing from carbon-intensive energy generation, facilitating mass transit and active transport options, also has the potential for significant public health benefits.

Climate change and health: impacts, vulnerability, adaptation and mitigation

Global climate change is progressing and health impacts have been observed in a number of countries, including Australia. The main health impacts will be due to direct heat exposure, extreme weather, air pollution, reduced local food production, food- and vectorborne infectious diseases and mental stress. The issue is one of major public health importance. Adaptation to reduce the effects of climate change involves many different sectors to minimise negative health outcomes. Wide-scale mitigation is also required, in order to reduce the effects of climate change. In addition, future urban design must be modified to mitigate and adapt to the effects of climate change. Strategies for mitigation and adaptation can create co-benefits for both individual and community health, by reducing non-climate-related health hazard exposures and by encouraging health promoting behaviours and lifestyles.

Air Pollution Monitoring and Mining Based on Sensor Grid in London

In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a two-layer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

Air Pollution Monitoring and Mining Based on Sensor Grid in London

In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a twolayer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR study)

BACKGROUND

Several studies have found an effect on mortality of between-city contrasts in long-term exposure to air pollution. The effect of within-city contrasts is still poorly understood.

OBJECTIVES

We studied the association between long-term exposure to traffic-related air pollution and mortality in a Dutch cohort.

METHODS

We used data from an ongoing cohort study on diet and cancer with 120,852 subjects who were followed from 1987 to 1996. Exposure to black smoke (BS), nitrogen dioxide, sulfur dioxide, and particulate matter < or = 2.5 microm (PM(2.5)), as well as various exposure variables related to traffic, were estimated at the home address. We conducted Cox analyses in the full cohort adjusting for age, sex, smoking, and area-level socioeconomic status.

RESULTS

Traffic intensity on the nearest road was independently associated with mortality. Relative risks (95% confidence intervals) for a 10-microg/m(3) increase in BS concentrations (difference between 5th and 95th percentile) were 1.05 (1.00-1.11) for natural cause, 1.04 (0.95-1.13) for cardiovascular, 1.22 (0.99-1.50) for respiratory, 1.03 (0.88-1.20) for lung cancer, and 1.04 (0.97-1.12) for mortality other than cardiovascular, respiratory, or lung cancer. Results were similar for NO(2) and PM(2.5), but no associations were found for SO(2).

CONCLUSIONS

Traffic-related air pollution and several traffic exposure variables were associated with mortality in the full cohort. Relative risks were generally small. Associations between natural-cause and respiratory mortality were statistically significant for NO(2) and BS. These results add to the evidence that long-term exposure to ambient air pollution is associated with increased mortality.

Does air pollution pose a public health problem for New Zealand?

Air pollution is increasingly documented as a threat to public health and a major focus of regulatory activity in developed and developing countries. Air quality indicators suggest New Zealand has clean air relative to many other countries. However, media releases such as 'Christchurch wood fires pump out deadly smog' and 'Vehicle pollution major killer' have sparked public health concern regarding exposure to ambient air pollution, especially in anticipation of increasing emissions and population growth. Recent evidence is presented on the effects of air quality on health, which has been aided by the application of urban airshed models and Geographic Information Systems (GIS). Future directions for research into the effects of air quality on health in New Zealand are discussed, including a national ambient air quality management project: HAPINZ--Health and Air Pollution in New Zealand.

Particulate matter in ambient air and mortality: toxicologic perspectives

U.S. regulations that set standards for acceptable concentrations of respirable particulate matter (PM) in outdoor air, particularly total fine particulate matter (PM(2.5)), are based largely on the belief that current concentrations cause death and illness, and that reducing these concentrations will save lives. Because the mortality risk estimates from important observational epidemiologic studies are extremely weak, derived from studies unable to control for relevant confounding causes, and inconsistent by location, toxicologic and clinical information is necessary to judge the likelihood and degree to which such findings are causal. Toxicologic data on typical forms of pollution-derived PM strongly suggest that current ambient concentrations in the U.S. are too small to cause significant disease or death. We review here the results of inhalation studies using concentrated ambient particles, diesel engine exhaust particulate matter, and sulfate and nitrate salts, and find no evidence that moderate concentrations are lethal. The expectation that lives will be saved by reducing ambient PM(2.5) in the U.S. is not supported by the weight of scientific evidence, although other bases for regulating PM may be justifiable.