An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

Ranking the environmental factors of indoor air quality of metropolitan independent coffee shops by Random Forests model

Independent coffee shops are the alternative workplaces for people working remotely from traditional offices but are not concerned about their indoor air quality (IAQ). This study aimed to rank the environmental factors in affecting the IAQ by Random Forests (RFs) models. The indoor environments and human activities of participated independent coffee shops were observed and recorded for 3 consecutive days including weekdays and weekend during the business hours. The multi-sized particulate matter (PM), particle-bound polycyclic aromatic hydrocarbons (p-PAHs), total volatile organic compounds (TVOCs), CO, CO₂, temperature and relative humidity were monitored. RFs models ranked the environmental factors. More than 20% of the 15-min average concentrations of PM₁₀, PM_2.5, and CO₂ exceeded the World Health Organization guidelines. Occupant density affected TVOCs, p-PAHs and CO₂ concentrations directly. Tobacco smoking dominated PM₁₀, PM_2.5, TVOCs and p-PAHs concentrations mostly. CO concentration was affected by roasting bean first and tobacco smoking secondly. The non-linear relationships between temperature and these pollutants illustrated the relative low concentrations happened at temperature between 22 and 24 °C. Tobacco smoking, roasting beans and occupant density are the observable activities to alert the IAQ change. Decreasing CO₂ and optimizing the room temperature could also be the surrogate parameters to assure the IAQ.

Trace elements in indoor dust-Children's health risk considering overall daily exposure

Indoor dust presents an important source of daily exposure to toxic elements. The present study reports for the first time the levels of Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Sn, Se, Sr, Tl, V, and Zn measured in dust samples collected from 10 kindergartens and 21 cars from Zagreb, Croatia. Based on the obtained data, we assessed the health risks from overall daily exposure to trace elements for children aged 2-6 years taking into account three pathways of dust intake-ingestion, dermal absorption, and inhalation. The median concentration of most elements was significantly higher in dust obtained from cars compared to kindergartens, especially in the cases of Co (11.62 vs. 3.60 mg kg^-1 ), Cr (73.55 vs. 39.89 mg kg^-1 ), Cu (186.33 vs. 26.01 mg kg^-1 ), Mo (8.599 vs. 0.559 mg kg^-1 ), Ni (37.05 vs. 17.38 mg kg^-1 ), and Sn (9.238 vs. 1.159 mg kg^-1 ). Oral intake was identified as the most important exposure pathway, except for Cr, Ni, and Sb where dermal contact was the main route of exposure. Health risk assessment indicated that no adverse effects are expected from overall exposure to trace elements. Although the cases of high exposure to toxic elements are not common in areas with no significant environmental pollutants, due to the health threat they may present even at low levels, their status should be carefully monitored.

A stochastic exposure model integrating random forest and agent-based approaches: Evaluation for PM2.5 in Jiangsu, China

This research proposes an Activity Pattern embedded Air Pollution Exposure Model (AP2EM), based on survey data of when, where, and how people spend their time and indoor/outdoor ratios for microenvironments. AP2EM integrates random forest and agent-based approaches to simulate the stochastic exposure to outdoor fine particulate matter (PM_2.5) along with indoor and in-vehicle PM_2.5 of outdoor origin. The R² of the linear regression between the model's calculations and personal measurement was 0.65, which was more accurate than the commonly-used aggregated exposure (AE) model and the outdoor exposure (OE) model. The population-weighted PM_2.5 exposure estimated by the AP2EM was 36.7 μg/m³ in Jiangsu, China, during 2014-2017. The OE model overestimated exposure by 54.0%, and the AE model underestimated exposure by 6.5%. These misestimate reflect ignorance of traditional studies on effects posed from time spent indoors (~85%) and doing low respiratory rate activities (~93%), problems of biased sampling, and neglecting low probability events. The proposed AP2EM treats activity patterns of individuals as chains and uses stochastic estimates to model activity choices, providing a more comprehensive understanding of human activity and exposure characteristics. Overall, the AP2EM is applicable for other air pollutants in different regions and benefits China's air pollution control policy designs.

Exposed to NO2 in the center, NOx polluters in the periphery: Evidence from the Paris region

Air pollution is the cause of many health problems. In cities, combustion vehicles are a major contributor to emissions of key air pollutants. While many studies have focused on populations exposed to pollutants and the resulting environmental and social inequalities, few compare exposures and contributions. In this research, the population of the Household Travel Survey of the Paris region is studied by confronting two elements: the average individual exposure to NO₂ during an average working day and the average traffic NO_x emitted during a day by the motorized trips for each resident surveyed. The dynamic exposure to NO₂ of each resident is estimated according to activities in an average working day. The results confirm an environmental inequality according to the place of residence: on average, the center residents contribute little to pollutant emissions but are highly exposed. Some categories of the population, including women and the socially disadvantaged, are the most affected by these inequalities.

Short-term effects of real-time individual fine particulate matter exposure on lung function: a panel study in Zhuhai, China

Fine particulate matter (PM_2.5) is still the primary air pollutant in most Chinese cities and its adverse effects on lung function have been widely reported. However, short-term effects of individual exposure to PM_2.5 on pulmonary expiration flow indices remain largely unknown. In this study, we examined the short-term effects of real-time individual exposure to PM_2.5 on lung function in a panel of 115 healthy adults. We measured individual real-time PM_2.5 exposure and lung function. Environmental PM_2.5 concentrations in the same period were collected from the nearest monitoring station. Generalized linear model was used to assess the effects of individual PM_2.5 exposure on lung function after adjusting for potential confounders. Individual PM_2.5 exposure ranged from 18.5 to 42.4 μg/m³ with fluctuations over time and ambient PM_2.5 concentrations presented a moderate trend of fluctuation at the same day. Except forced expiratory volume in 1 s (FEV₁) decline related to 2-h moving average PM_2.5 exposure, no significant associations between individual PM_2.5 exposure and other volume indices including forced vital capacity (FVC) and FEV₁/FVC ratio were observed. The adverse effects of individual PM_2.5 exposure on pulmonary expiration flow indices including peak expiratory flow (PEF), maximal mid-expiratory flow (MMF) and forced expiratory flow at 50%, and 75% of vital capacity (FEF_50% and FEF_75%) were observed to be strongest at 2 moving average hours and could last for 24 h. Stratified analysis showed greater and longer effects among participants who were aged over 40 years, males, or smokers. These findings suggested that individual PM_2.5 exposure was significantly associated with altered lung function, especially with pulmonary expiration flow indices decline, which was strongest at 2 moving average hours and could last for 24 h.

Personal exposure to fine particulate matter and blood pressure: Variations by particulate sources

Fine particulate matter (PM_2.5) is a complex mixture of components which has been associated with various cardiovascular effects, such as elevated blood pressure (BP). However, evidences on specific sources behind these effects remain uncertain. Based on 140 72-h personal measurements among a panel of 36 health college students in Shanghai, China, we assessed associations between source-apportioned PM_2.5 exposure and BP changes. Based on personal filter samples, PM_2.5 source apportionment was conducted using Positive Matrix Factorization (PMF) model. Linear mixed-effects models were applied to evaluate associations of source-specific PM_2.5 exposure with BP changes. Seven sources were identified in PMF analysis. Among them, secondary sulfate (41%) and nitrate (24%) sources contributed most to personal PM_2.5, followed by industrial emissions (15%), traffic-related source (10%), coal combustion (6.2%), dust (2.4%) and aged sea salt (1.1%). We found nitrate, traffic-related source and coal combustion were significantly associated with increased BP. For example, an interquartile range increase in PM_2.5 from traffic-related source was significantly associated with increase in systolic BP [1.5 (95% CI: 0.26, 2.7) mmHg], diastolic BP [1.2 (95% CI: 0.10, 2.2) mmHg] and mean arterial pressure [1.2 (95% CI: 0.15, 2.2) mmHg]. This is the first investigation linking personal PM_2.5 source profile and BP changes. This study provides evidence that several anthropogenic emissions (especially traffic-related emission) may be particularly responsible for BP increases, and highlights that the importance of development of health-oriented PM_2.5 source control strategies.

Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities

Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children. We apply nested WRF-CAMx modelling of PM_2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures. Even though levels in traffic and outdoors were considerably higher, 80-84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 μg/m³) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 μg/m³). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.

Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK

Amid the COVID-19 pandemic, a nationwide lockdown was imposed in the United Kingdom (UK) on March 23, 2020. These sudden control measures led to radical changes in human activities in the Greater London Area (GLA). During this lockdown, transportation use was significantly reduced and non-key workers were required to work from home. This study aims to understand how population exposure to PM_2.5 and NO₂ changed spatially and temporally across London, in different microenvironments, following the lockdown period relative to the previous three-year average in the same calendar period. Our research shows that population exposure to NO₂ declined significantly (52.3% ± 6.1%), while population exposure to PM_2.5 showed a smaller relative reduction (15.7% ± 4.1%). Changes in population activity had the strongest relative influence on exposure levels during morning rush hours, when prior to the lockdown a large percentage of people would normally commute or be at the workplace. In particular, a very high exposure decrease was observed for both pollutants (approximately 66% for NO₂ and 19% for PM_2.5) at 08:00am, consistent with the radical changes in population commuting. The infiltration of outdoor air pollution into housing modifies the degree of exposure change both temporally and spatially. Moreover, this study shows that the impacts on air pollution exposure vary across groups with different socioeconomic status (SES), with a disproportionate positive effect on the areas of the city home to more economically deprived communities.

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

Health Impact of Air Pollution from Shipping in the Baltic Sea: Effects of Different Spatial Resolutions in Sweden

In 2015, stricter regulations to reduce sulfur dioxide emissions and particulate air pollution from shipping were implemented in the Baltic Sea. We investigated the effects on population exposure to particles <2.5 µm (PM_2.5) from shipping and estimated related morbidity and mortality in Sweden’s 21 counties at different spatial resolutions. We used a regional model to estimate exposure in Sweden and a city-scale model for Gothenburg. Effects of PM_2.5 exposure on total mortality, ischemic heart disease, and stroke were estimated using exposure–response functions from the literature and combining them into disability-adjusted life years (DALYS). PM_2.5 exposure from shipping in Gothenburg decreased by 7% (1.6 to 1.5 µg/m³) using the city-scale model, and 35% (0.5 to 0.3 µg/m³) using the regional model. Different population resolutions had no effects on population exposures. In the city-scale model, annual premature deaths due to shipping PM_2.5 dropped from 97 with the high-sulfur scenario to 90 in the low-sulfur scenario, and in the regional model from 32 to 21. In Sweden, DALYs lost due to PM_2.5 from Baltic Sea shipping decreased from approximately 5700 to 4200. In conclusion, sulfur emission restrictions for shipping had positive effects on health, but the model resolution affects estimations.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO₂ and 19% more to PM_2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO₂ and PM_2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO₂ and 15% for PM_2.5). Results for population-weighted exposure revealed exposure to PM_2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO₂ and PM_2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Quantifying the Health Burden Misclassification from the Use of Different PM2.5 Exposure Tier Models: A Case Study of London

Exposure to PM_2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM_2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM_2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration (i.e., approximately 37%) and as a result the largest change in mortality (i.e., health burden/mortality misclassification) was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174–1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments.