Air Pollution Episodes Research Articles

Prediction of Particulate Matter (PM10) during High Particulate Event in Peninsular Malaysia using Novel Hybrid Model

High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles present can be inhaled, leading to respiratory diseases and other health problem. Knowing the adverse effects of air pollution episodes to human health, it is crucial to create suitable models that can effectively and accurately predict air pollution concentration. This study proposed a hybrid model for forecasting the next day PM10 concentration in peninsular Malaysia namely Shah Alam, Nilai, Bukit Rambai and Larkin. Hourly air pollutant concentration (PM10, NOx, NO2, SO2, CO, O3) and meteorological parameters (RH, T, WS) during the HPE events in 1997, 2005, 2013 and 2015 were used. Support Vector Machine (SVM) and Quantile Regression (QR) was combined to construct a hybrid models (SVM-QR) to reduce the number of input variables. Performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Index of Agreement (d2) were used to evaluate the performance of the predictive models. SVM-QR model resulted good performance in all areas. SVM-3 was selected as the best model at Bukit Rambai (MAE=5.72, RMSE=9.71) and Shah Alam (MAE=11.89, RMSE=22.66), while SVM-1 as the best model at Larkin and Nilai with the value (MAE=7.22, RMSE=13.38) and (MAE=6.88, RMSE=11.84), respectively. This strategy was proven to help reducing the complexity of the model and enhance the predictive capacity of the model.

Strategy Design of PM2.5 Controlling for Northern Thailand

The emission of fine particulate matter (PM2.5) in dry season from the open biomass burning has caused a long-term negative impact on residents’ health in Northern Thailand. This study takes Chiang Mai and Chiang Rai provinces in Northern Thailand as the study areas to identify pollution episodes, analyze PM2.5 source trajectories, and finally propose pollution control strategies accordingly. PM2.5 levels during 2019–2021 of three representative air pollution monitoring stations (i.e., Chaing Mai-35T, Chiang Rai-57T, and Mae Sai-73T) in these two provinces were collected and analyzed. The Air Quality Index (AQI) defined by PM2.5 level higher than 91 µg m-3 causing serious adverse health effects was adopted to define periods having pollution levels, and the days of the air pollution episodes were identified. Based on these episodes, we applied the backward trajectory model to identify the sources of pollutants. Results showed that PM2.5 levels were significantly higher between February to April compared with other months during 2019–2021 at all three monitoring stations, indicating the severity of PM2.5 episode during the dry season. The backward trajectory demonstrated that air mass transported through the Northern Thailand or nearby mountain areas (categorized as long- or short-transport-distance) contributed up to 21.6% and 75.9% of the total air mass, respectively. Although residents in these mountainous areas are accustomed to the biomass burning, we suggested that there should be urgent needs for the improvement of the long-term air quality in these two provinces. Therefore, this study proposes some control strategies including improvement of prevention knowledge, increase of the risk perception, cultivation of the protection behavior, and intensification of the social influence. In addition to reducing biomass burning pollution, this improvement plan also has a co-benefit of achieving resources recycling concomitantly. Providing effective management strategies may reduce the adverse health effects to Thai residents.

Beehive Fireworks Festival Effect on the Nearby Atmospheric PM2.5 Level

Sudden short-term air pollution episode is now widely considered to harm human health. Previous research has found that firework activities rapidly raise the PM2.5 level in the ambient air. This study investigates the influence of Yanshuei Beehive Fireworks Festival on atmospheric PM2.5 from February 9th to 12th, 2017. The PM2.5 samples were gathered at 8 sampling sites around Yanshuei and Xinying before (background (B)), trial (T), during (D) and after (A) beehive firework display periods during the Yanshuei Beehive Fireworks Festival. The temporospatial differences of atmospheric PM2.5 before and after fireworks activities were explored. The atmospheric PM2.5 level in major activity areas was significantly higher from the background level in the trial and festival periods, and even after the activity. The study revealed that PM2.5 level reached the highest value of 327 μg m-3 at the major activity areas, which is 6.6 and 5.9 times those at upwind (49.8 μg m-3) and downwind (55.5 μg m-3) sites, respectively. Additionally, the T/B and D/B ratios were 3.01 and 7.19, respectively, around the major activity area. Conversely, the wind rose diagrams and contour lines of PM2.5 concentrations evaluated using Surfer 10.0 around the ambient air demonstrate that the atmospheric PM2.5 levels at Yanshuei and Xinying were similar to each other (35–45 μg m-3). However, the PM2.5 hardly diffused to distant places and accumulated in the local area around the boundary between Yanshuei and Xinying during the Beehive Fireworks Festival, since the wind speed was usually low or even stayed calm. The iso-concentration contour maps show that K+, Cl-, Mg2+ and NO3- are related to firework release during the festival.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021.

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

A numerical simulation of PM<sub>2.5</sub> concentration using the WRF-Chem model during a high air pollution episode in 2019 in Jakarta, Indonesia

<span>Jakarta, as a megapolitan city, is always crowded with thousands of vehicles every day which results in decreased air quality due to combustion emissions and may have a significant impact on human health. Particulate matter (PM<sub>2.5</sub>) is a pollutant that has an aerodynamic diameter of fewer than 2.5 micrometers and very easy to enter the human respiratory system so it can affect health. In the dry season, rain as the main natural mechanism for reducing PM<sub>2.5</sub> occurs very rarely, causing an accumulation of PM<sub>2.5</sub> concentrations in the atmosphere. The weather research and forecasting model coupled with chemistry (WRF-Chem) model is a dynamic model that works with atmospheric chemistry combined with meteorological variables simultaneously. This study aims to simulate the concentration of PM<sub>2.5</sub> in Jakarta during the high air pollution episode from 20 to 29 June 2019 with the WRF-Chem model based on the T1-MOZCART chemical scheme. Spatial analysis was conducted to determine the distribution of PM<sub>2.5</sub> concentrations during high air pollution episodes in Jakarta. Validation of the simulation model was based on three observation sites, one in South Jakarta and two in Central Jakarta. The results showed that the highest correlation is 0.3 and the lowest root mean square error (RMSE) is 26.4, while the simulations still tend to overestimate the PM<sub>2.5</sub> concentration.</span>

Using a Pollution-to-Risk Method to Evaluate the Impact of a Cold Front: A Case Study in a Downstream Region in Southeastern China

Cold fronts frequently intrude China in winter, causing air pollution episodes in downwind regions. Fine particulate matter (PM2.5) has been used as a major proxy of air pollution to examine the impacts of cold fronts. Compared to particles, gaseous pollutants can cause comparable or even higher levels of short-term health risks. In this study, a pollution-to-risk model was used to systematically evaluate the impacts of cold fronts on the combined health risks of air pollution mixtures, including PM2.5, nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). Dominant pollutants that caused significant mortality risks during a cold frontal passage in December 2019 over Fuzhou, a downwind city in southeastern China, were then examined. Under northerly frontal airflows, a pollution belt propagated southwards. In Fuzhou, two pollution peaks existed during the cold frontal passage. At the first peak, convergence and stagnant air in the frontal zone rapidly accumulated local air pollutants. The dominant pollutants that caused the mortality risk were identified as NO2 and PM2.5, both of which contributed 45% to the total risk. At the second peak, advection transported a significant amount of secondary pollutants from the upwind regions. Although PM2.5 was the dominant pollutant at this peak, gaseous pollutants still accounted for 34% of the total risk. Our risk analyses underscore the significant health impacts of gaseous pollutants during cold frontal passages in winter. The results generated from this study will help guide environmental policy makers in forming and improving air pollution control strategies during pollution episodes.

A Study of Air Pollution Episode Over Delhi National Capital Region, India During October 29, 2016- November 03, 2016

Northern India has an increase in surface air pressure during winter; this causes the atmosphere to clearout, resulting in the production of significant surface inversions. High-pressure zones are also calm andwind direction changes only when a western disturbance enters the area. The wind is substantially strongerwhen there is a low-pressure system. As a result, the pollutants in northern India are regulated by a sequenceof high- and low-pressure weather situations. October 29, 2016, was when the wind direction unexpectedlyshifted from NW to NE and then to N. Wind speeds dropped from 1-6 km/h to near-calm during this time,which indicated the passing of a high-pressure zone, based on the readings of the pressure gauges. Pollutionfrom northern states was brought into the national capital region by a combination of low wind speed andthe wind’s direction. NCR was clogged with high levels of particulate matter during Diwali celebrations,which were exacerbated by heavy traffic, factory production, and firecrackers. The grave situation persisteduntil the wind shifted directions and increased its velocity. On the monostatic acoustic radar installed atREMTech, Shamli, detected the persistence of a substantially raised layer over the region, implying thatvertical mixing of pollutants was severely harmed or limited. As a result of this state, pollutants accumulated,resulting in a severe bout of air pollution. The findings of this study details an incident of such air pollution happened over Delhi NCR.

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China

Particulate nitrate plays an increasingly important role in the formation of air pollution process, while the main mechanisms of nitrate concentration change are different in each stage, same as the driving factors. In this study, we proposed an episode-based analysis to illustrate the typical nitrate evolution patterns and identify the possible impacting factors in different evolution stages. Applying into twelve air pollution episodes, three typical patterns of nitrate evolution were abstracted, and the corresponding conceptual models were constructed. All the pollution episodes were grouped by their evolving shapes, which were driven by physical and chemical processes. Episodes started slowly typically arose from gradual pollutant accumulation, both locally and regionally, and chemical formation under high humidity. Type 1 (“hump-shaped type”), accounting for 66.3 % of the total episode durations, including two “peak” concentrations, displays a rapid growth rate which could up to 4.6 μg m−3 h−1 in average, mainly relying on the sharp drop in the planetary boundary layer height. Short scavenging processes and thoroughly dissipated stages of the pollution episodes always accompanied by strong north wind affected by Siberia-Mongolia cold current. Type 2 (“triangle-shaped type”, 24.3 %) shows a gentle growth rate and short duration. Compared with Type 1, chemical process may be more important “source” for the increase of nitrate concentration during Type 2. Type 3 (“trapezoid-shaped type”, 9.4 %) presents a long platform stage, during which high humidity (RH > 90 %) provides favorable conditions for wet removal and secondary production, and the updraft can carry pollutants to high altitude. The source and sink are roughly balanced for Type 3. Our study highlights the importance of pattern identification for understanding the nitrate evolution behavior, it may also provide insights for pollution prediction and scientific mitigation strategies.

Modeling Air Pollution Using Partially Varying Coefficient Models with Heavy Tails

The increase in air pollution levels in recent decades around the world has caused a negative impact on human health. A recent investigation by the World Health Organization indicates that nine out of ten people on the planet breathe air containing high levels of pollutants and seven million people die each year from this cause. This problem is present in several cities in South America due to dangerous levels of particulate matter present in the air, particularly in the winter period, making it a public health problem. Santiago in Chile and Lima in Peru are among the ten cities with the highest levels of air pollution in South America. The location, climate, and anthropogenic conditions of these cities generate critical episodes of air pollution, especially in the coldest months. In this context, we developed a semiparametric model to predict particulate matter levels as a function of meteorological variables. For this, we discuss estimation and diagnostic procedures using a Student’s t-based partially varying coefficient model. Parameter estimation is performed through the penalized maximum likelihood method using smoothing splines. To obtain the parameter estimates, we present a weighted back-fitting algorithm implemented in R-project and Matlab software. In addition, we developed local influence techniques that allowed us to evaluate the potential influence of certain observations in the model using four different perturbation schemes. Finally, we applied the developed model to real data on air pollution and meteorological variables in Santiago and Lima.

Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018-2020: Implications for Environmental Justice in the Context of Climate Change.

Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018-2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 μg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities.

Observed sensitivities of PM2.5 and O3 extremes to meteorological conditions in China and implications for the future.

Frequent extreme air pollution episodes in China accompanied with high concentrations of particulate matters (PM2.5) and ozone (O3) are partly supported by meteorological conditions. However, the relationships between meteorological variables and pollution extremes can be poorly estimated solely based on mean pollutant level. In this study, we use quantile regression to investigate meteorological sensitivities of PM2.5 and O3 extremes, benefiting from nationwide observations of air pollutants over 2013-2019 in China. Results show that surface winds and humidity are identified as key drivers for high PM2.5 events during both summer and winter, with greater sensitivities at higher percentiles. Higher humidity favors the hydroscopic growth of particles during winter, but it tends to decrease PM2.5 through wet scavenging during summer. Surface temperature play dominant role in summer O3 extremes, especially in VOC-limited regime, followed by surface winds and radiation. Sensitivities of O3 to meteorological conditions are relatively unchanging across percentiles. Under the fossil-fueled development pathway (SSP5-8.5) scenario, meteorological conditions are projected to favor winter PM2.5 extremes in North China Plain (NCP), Yangtze River Delta (YRD) and Sichuan Basin (SCB), mainly due to enhanced surface specific humidity. Summer O3 extremes are likely to occur more frequently in the NCP and YRD, associated with warmer temperature and stronger solar radiation. Besides, meteorological conditions over a relatively longer period play a more important role in the formation of pollution extremes. These results improve our understanding of the relationships between extreme PM2.5 and O3 pollution and meteorology, and can be used as a valuable reference of model predicted air pollution extremes.

Prenatal and early postnatal exposure to air pollution associations with primary care and prescription usage

BACKGROUND AND AIM: Health consequences of air pollution in early life have been mostly studied in hospital settings, with less focus put on primary care and prescribed medications. The aim of this study was to investigate general practitioner (GP) consultations and prescriptions dispensed following exposure in utero and in the first year of life to the Hazelwood coal mine fire, a severe air pollution episode lasting six weeks. METHODS: The Victorian Perinatal Data Collection was linked to the Medicare Benefits Schedule and the Pharmaceutical Benefits Scheme for a cohort of children born in the Latrobe Valley, Australia between March 2012 and December 2015. Modelled estimates of fire-related PM2.5 exposure were assigned at the residential address at birth. The total numbers of GP visits and of prescriptions of antibiotics, asthma inhalers and steroid skin creams dispensed in the first year of life or in the year following the fire were extracted for children exposed in utero and in infancy (<1 year old), respectively. Negative binomial regression models estimated associations between each outcome and exposure to fire-related PM2.5, with adjustment made for potential confounders. RESULTS: Higher levels of PM2.5 in utero were associated with increased asthma inhalers dispensed (IRR=1.07, 95%CI=1.00-1.14 per 100µg/m³ of cumulative PM2.5; IRR=1.27, 95%CI=1.01-1.58 per 50µg/m3 of peak 24-h PM2.5) in infancy. Surprisingly, a cumulative exposure >25 µg/m³ in infancy was linked to fewer GP visits (IRR=0.83, 95%CI=0.68-0.99) in the year following the fire. CONCLUSIONS: Our findings indicate that prenatal exposure to time-limited severe air pollution could be linked with increased need of asthma medications in infancy. They also suggest that such exposure in infancy could be associated with less GP consultations following extreme events, which could be due to increased needs of specialised and hospital care. KEYWORDS: Particulate matter; Long-term exposure; Infections; Asthma

Assessing the effects of multivariate functional outlier identification and sample robustification on identifying critical PM2.5 air pollution episodes in Medellín, Colombia

Identification of critical episodes of environmental pollution, both as a outlier identification problem and as a classification problem, is a usual application of multivariate functional data analysis. This article addresses the effects of robustifying multivariate functional samples on the identification of critical pollution episodes in Medellín, Colombia. To do so, it compares 18 depth-based outlier identification methods and highlights the best options in terms of precision through simulation. It then applies the two methods with the best performance to robustify a real dataset of air pollution (PM2.5 concentration) in the Metropolitan Area of Medellín, Colombia and compares the effects of robustifying the samples on the accuracy of supervised classification through the multivariate functional DD-classifier. Our results show that 10 out of 20 methods revised perform better in at least one kind outliers. Nevertheless, no clear positive effects of robustification were identified with the real dataset.

Effectiveness of a Protocol to Reduce Children's Exposure to Particulate Matter and NO2 in Schools during Alert Days.

Reducing children’s exposure to air pollutants should be considered a primary goal, especially for the most vulnerable subjects. The goal of this study was to test the effectiveness of applying a protocol in the event of alert days, i.e., days with forecasted PM10 levels above the EU limit value (50 µg/m3). The test was conducted, before the onset of SARS-CoV-2 restrictions, in a classroom of a primary school in Parma (Italy)—a highly polluted area in Northern Italy. The protocol included indications for the frequency of opening windows and doors, as well as the activation of an air purifier. Teachers and students were asked to apply the protocol only in the event of alert days, while no indications were provided for non-alert days. A monitoring system measuring PM1, PM2.5, PM10, CO2, and NO2 was deployed in the classroom. Measurements of the same parameters were also performed outdoors near the school. The application of the protocol reduced the indoor/outdoor (I/O) ratio for all toxic pollutants. The reduction was also remarkable for PM10—the most critical air quality parameter in the study area (1.5 and 1.1 for non-alert and alert days, respectively). Indoor concentrations of PM10—especially during non-alert days—were often higher than outdoors, showing a major contribution from resuspension due to the movement of people and personal cloud. The protocol did not cause any increase in indoor CO2 levels. Our findings showed that the application of a ventilation protocol together with the contribution of an air purifier may represent an effective way to reduce children’s exposure to air pollution during severe air pollution episodes. Considering the onset of COVID-19 and the airborne transmission of pathogens, this protocol now has more meaningful implications for children’s welfare, and can be integrated with protocols designed as measures against the spread of SARS-CoV-2.

Source apportionment of air pollution in European urban areas: Lessons from the ClairCity project

Air pollution has become a major threat to human health in the last decades, with an increase of acute air pollution episodes in many cities worldwide. Source apportionment modelling provides valuable information on the contribution from different emission source sectors and source regions to distinct air pollutants concentrations. In this study, the CAMx model, with its PSAT tool, was applied to quantify the contribution of multiple source areas, categories and pollutant types to ambient air pollution, namely to PM and NO2 concentrations, over six European urban areas: Bristol (United Kingdom), Amsterdam (The Netherlands), Ljubljana (Slovenia), Liguria Region (Italy), Sosnowiec (Poland) and Aveiro Region (Portugal). Results indicate overall higher annual NO2 and PM concentrations located in the urban centres of the case studies. A comparison between the different areas showed that Liguria is the region with highest NO2 annual mean concentrations, while Ljubljana, Liguria Region and Sosnowiec are the case studies with the highest PM annual mean concentrations. The annual average contributions denote a major influence from road transport to NO2 concentrations, with up to 50%, except in Aveiro region, where road transport presents a lower contribution to NO2 concentrations, and the greatest contributor is the industrial combustion and processes sector with 45%. These results indicate a negligible contribution of the transboundary transport to NO2 concentrations, highlighting the relevance of local sources, while for PM concentrations the transboundary transport is the major contributor. The results highlight the relevance of long-range transport of PM across Europe. The transboundary transport reduces its importance during winter, when residential and commercial combustion increases its contribution. In the case of the Aveiro region, the industrial combustion and processes sector also plays an important contribution to PM concentrations.

Long-term trends of atmospheric hot-and-polluted episodes (HPE) and the public health implications in the Pearl River Delta region of China

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) – an episode with simultaneously high levels of air pollution and temperature – which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and ozone (O3) for June to October over 20 years (2000–2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000–2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010–2019). The northern cities in the PRD region had higher levels of PM2.5 and O3 than their southern counterparts. During HPEs, regional daily PM2.5 exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O3 exceeded the WHO O3 standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357–623,770) and 52,231 (95%CI: 26,116–78,346) excessive deaths were respectively attributable to exposure to PM2.5 and O3 in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India.

Air quality has a tremendous impact on India's health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 µm). We have included the main aspects of air quality models in this research i.e., less computational time (7min approximately using I5-1035G1, 1.19GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.

Assessment of the Feasibility of a Future Integrated Larger-Scale Epidemiological Study to Evaluate Health Risks of Air Pollution Episodes in Children.

Air pollution exposure can lead to exacerbation of respiratory disorders in children. Using sensitive biomarkers helps to assess the impact of air pollution on children’s respiratory health and combining protein, genetic and epigenetic biomarkers gives insights on their interrelatedness. Most studies do not contain such an integrated approach and investigate these biomarkers individually in blood, although its collection in children is challenging. Our study aimed at assessing the feasibility of conducting future integrated larger-scale studies evaluating respiratory health risks of air pollution episodes in children, based on a qualitative analysis of the technical and logistic aspects of a small-scale field study involving 42 children. This included the preparation, collection and storage of non-invasive samples (urine, saliva), the measurement of general and respiratory health parameters and the measurement of specific biomarkers (genetic, protein, epigenetic) of respiratory health and air pollution exposure. Bottlenecks were identified and modifications were proposed to expand this integrated study to a higher number of children, time points and locations. This would allow for non-invasive assessment of the impact of air pollution exposure on the respiratory health of children in future larger-scale studies, which is critical for the development of policies or measures at the population level.

Spatial distribution of particulate matter on winter nights in Temuco, Chile: Studying the impact of residential wood-burning using mobile monitoring

Temuco, a medium-sized city in the south of Chile, is heavily impacted by residential wood-burning particularly in winter, which causes strong episodes of air pollution. We spatially characterized the distribution of particulate matter (PM) during winter nights in Temuco using mobile measurements. In winter 2016 (June 8-July 15th), continuous monitors for PM ≤ 2.5 μm (PM2.5) and ultrafine particles <0.1 μm (UFP) were deployed in a vehicle along with a positioning system during 20 nights (20:00 to 2:00) in assigned routes that crossed the city and additional sampling was conducted at a central site. Measurements were expressed as concentrations and ratios to central site concentrations and collapsed by spatial grids of 50, 100, 200, and 400 m. Average PM2.5 concentrations were ∼100 μg m−3, while UFP ∼30,000 counts cm−3. Some neighborhoods had concentrations of PM2.5 nearly double those measured at a centrally located site, while other neighborhoods had less than half the measured level. We identified hotspots and cold spots of PM2.5 and UFP throughout the urban area—and observed some neighborhoods in which over 20% of the area was categorized as a hotspot for one of the pollutants (14 for PM2.5 and 11 for UFP out of 34). Using mobile monitoring—a relatively simple and inexpensive methodology— we characterized the spatial distribution of pollutants and likely clusters for the time period, which may guide future spatial campaigns and help targeting local interventions aimed at air pollution mitigation.

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks.

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.