Association between microenvironment air quality and cardiovascular health outcomes

Comparison of cooking emissions mitigation between automated and manually operated air quality interventions in one-bedroom apartments

We implemented a crossover study design exposing 15 participants to two indoor air quality conditions in the Well Living Lab. The first condition, the Standard Control Condition, resembled the ventilation and air supply of a typical home in the USA with a manually operated stove hood. The second condition, Advanced Control, had an automated: (i) stove hood, (ii) two portable air cleaners (PAC), and (iii) bathroom exhaust. The PM2.5 sensors were placed in the kitchen, living room, bedroom, and bathroom. Once the sensor detected a PM2.5 level of 15 μg/m3 or higher, an air quality intervention (stove hood, PAC or bathroom exhaust) in that space was activated and turned off when the corresponding PM2.5 sensor had three consecutive readings below 6 μg/m3. Advanced Control in the overall apartment reduced PM2.5 concentration by 40% compared to the Standard Control. The PM2.5 concentration difference between Advanced and Standard Control was ~ 20% in the kitchen. This can be attributed to using the stove hood manually in 66.5% of cooking PM2.5 emission events for 323.6 h compared to 88 h stove hood used in automated mode alongside 61.9 h and 33.7 h of PAC use in living room and bedroom, respectively.

When and where to exercise: An assessment of personal exposure to urban tropical ambient airborne pollutants in Singapore

BACKGROUND

Physical activity is associated with health benefits and has been shown to reduce mortality risk. However, exposure to high levels of ambient fine particulate matter (PM2.5) during exercise can potentially reduce the health benefits of physical activity. This study aims to assess and compare the PM2.5 concentrations of different exercise venues in Singapore by their location attributes and time of day.

METHODS

Personal PM2.5 exposures (μg/m3) at 24 common outdoor exercise venues in Singapore over 49 sampling days were collected using real-time personal sensors from September 2017 to January 2020. Wilcoxon rank-sum test and Kruskal-Wallis test were used to compare PM2.5 concentrations between different timings (peak (0700-0900; 1800-2000) vs. non-peak (0600-0700; 0900-1800; 2000-2300); weekend vs. weekday), and location attributes (near major roads (<50 m) vs. away from major roads (≥50 m)). Multivariable linear regression models were used to assess the associations between location attributes, timings and ambient PM2.5 with personal PM2.5 concentration, adjusting for potential confounders.

RESULTS

Compared with peak hours, exercising during non-peak hours was associated with a significantly lower PM2.5 exposure (median, 17.8 μg/m3 during peak vs. 14.5 μg/m3 during non-peak; P = 0.006). Exercise venues away from major roads have significantly lower PM2.5 concentrations as compared to those located next to major roads (median, 14.4 μg/m3 away from major roads vs. 18.5 μg/m3 next to major roads; P < 0.001). Individuals who exercised in parks experienced the highest PM2.5 exposure (median, 55.0 μg/m3) levels in the afternoon during 1400-1500. Furthermore, ambient PM2.5 concentration was significantly and positively associated with personal PM2.5 exposure (β = 0.85, P < 0.001).

CONCLUSIONS

Our findings suggest that exercising outdoors in the urban environment exposes individuals to differential levels of PM2.5 at different times of the day. Further research should investigate a wider variety of outdoor exercise venues, explore different types of air pollutants, and consider the varying activity patterns of individuals.

Integrated assessment of personal monitor applications for evaluating exposure to urban stressors: A scoping review

Urban stressors pose a health risk, and individual-level assessments provide necessary and fine-grained insight into exposure. An ever-increasing amount of research literature on individual-level exposure to urban stressors using data collected with personal monitors, has called for an integrated assessment approach to identify trends, gaps and needs, and provide recommendations for future research. To this end, a scoping review of the respective literature was performed, as part of the H2020 URBANOME project. Moreover, three specific aims were identified: (i) determine current state of research, (ii) analyse literature according with a waterfall methodological framework and identify gaps and needs, and (iii) provide recommendations for more integrated, inclusive and robust approaches. Knowledge and gaps were extracted based on a systematic approach, e.g., data extraction questionnaires, as well as through the expertise of the researchers performing the review. The findings were assessed through a waterfall methodology of delineating projects into four phases. Studies described in the papers vary in their scope, with most assessing exposure in a single macro domain, though a trend of moving towards multi-domain assessment is evident. Simultaneous measurements of multiple stressors are not common, and papers predominantly assess exposure to air pollution. As urban environments become more diverse, stakeholders from different groups are included in the study designs. Most frequently (per the quadruple helix model), civil society/NGO groups are involved, followed by government and policymakers, while business or private sector stakeholders are less frequently represented. Participants in general function as data collectors and are rarely involved in other phases of the research. While more active involvement is not necessary, more collaborative approaches show higher engagement and motivation of participants to alter their lifestyles based on the research results. The identified trends, gaps and needs can aid future exposure research and provide recommendations on addressing different urban communities and stakeholders.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

City-scale assessment of long-term air quality impacts on the respiratory and cardiovascular health

Background

The impact of the urban environment on human health is a contemporary subject of environmental research. Air pollution is often considered a leading environmental driver. However, a plethora of other factors within the urban exposome may be involved. At the same time, the resolution of spatial data is also an important facet to consider. Generally, systematic tools for accurate health risk assessment in the urban environment are missing or are not implemented.

Methods

The long-term impact of air quality (PM10, PM2.5, NO2, benzene, and SO2) on respiratory and cardiovascular health was assessed with a log-linear model. We used the most accurate health data in high city scale spatial resolution over the period 2010 to 2018. Selected external exposome parameters were also included in the analysis.

Results

Statistically significant associations between air pollution and the health of the urban population were found. The strongest association was between benzene and the incidence of bronchitis in the adult population [RR 1.552 95% CI (1.415-1.704) per 0.5 μg/m3 change in benzene concentration]. A similar relation was observed between NO2 and the same health condition [RR 1.483 95% CI (1.227-1.792) per 8.9 μg/m3 of change in NO2]. Other weaker associations were also found between asthma in children and PMs, NO2, or benzene. Cardiovascular-related hospitalizations in the general population were linked with NO2 [RR 1.218 95% CI (1.119-1.325) per 9.7 μg/m3 change in NO2]. The remaining pollutants were slightly less but still significantly associated with cardiovascular-related hospitalizations.

Conclusion

Our findings are mostly highly statistically significant (p ≤ 0.001) and are in line with current literature on the adverse effects of air pollution on the human population. The results highlight the need for continual improvements in air quality. We propose the implementation of this approach as a systematic tool for the investigation of possible health risks over a long period of time. However, further research involving other variables is an essential step toward understanding the complex urban exposome and its implications for human health. An increase in data spatial resolution is especially important in this respect as well as for improving city health risk management.

Short-term effects of indoor and outdoor air pollution on the lung cancer morbidity in Henan Province, Central China

Lung cancer is one of the most common cancer types and a major cause of death. The relationship between lung cancer morbidity and exposure to air pollutants is of particular concern. However, the relationship and difference in lung cancer morbidity between indoor and outdoor air pollution effects remain unclear. In this paper, the aim was to comprehensively investigate the spatial relationships between the lung cancer morbidity and indoor-outdoor air pollution in Henan based on the standard deviation ellipse, spatial autocorrelation analysis and GeoDetector. The results indicated that (1) the spatial distribution of lung cancer morbidity was related to the geomorphology, while high-morbidity areas were concentrated in the plains and basins of Central, Eastern and Southern Henan. (2) Among the selected outdoor air pollutants, PM_2.5, NO₂, SO₂, O₃ and CO were significantly correlated with the lung cancer morbidity. The degree of indoor air pollution was measured by the use of heating energy, and the proportions of coal-heating households, households with coal/biomass stoves and households with heated kangs were highly decisive in regard to the lung cancer morbidity. (3) The interaction between two factors was more notable than a single factor in explaining the lung cancer morbidity. Moreover, the interaction type was mainly nonlinear enhancement, and the proportion of households with coal/biomass stoves imposed the strongest interaction effect on the other factors.

Acute effects of personal exposure to fine particulate matter on salivary and urinary biomarkers of inflammation and oxidative stress in healthy adults

Non-invasive bio-samples, such as saliva and urine, are promising tools for assessment of inflammation and oxidative stress biomarkers. Few studies have investigated potential responses of those biomarkers towards short-term PM_2.5 exposure. We conducted a longitudinal study with 4 repeated examinations among 40 healthy, nonsmoking adults in Shanghai, China. Personal samplings were performed for PM_2.5 exposure assessment. Then, five biomarkers, including C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), alpha-1 antitrypsin (A1AT) in saliva and 8-Iso-Prostaglanding F_2α (8-iso-PGF_2α), total antioxidant capacity (TAC) in urine, were measured. We fitted linear mixed-effect models to evaluate short-term effect of personal PM_2.5 exposure on salivary and urinary biomarkers, adjusting for potential confounders of meteorology, sociodemographic characteristics and biomarker detection. We also explored sensitive time windows of exposure for different biomarkers. We found robust associations of salivary CRP, TNF-α, and urinary 8-iso-PGF_2α with PM_2.5 exposure, and responses of salivary inflammatory markers occurred more acutely than urinary oxidative stress markers. For instance, a 10 μg/m³ increase in PM_2.5 was associated with an elevation of 5.49% (95% CI: 1.17%, 9.99%) in CRP and 7.05% (95% CI: 1.29%, 13.13%) in TNF-α both at lag 12 h, and 6.97% (95% CI: 1.33%, 12.92%) in 8-iso-PGF_2α at lag 01 d. Based on non-invasive samples, this study provided evidence on effect of PM_2.5 exposure on responses of systematic inflammation and oxidative stress. Sub-daily (6-12 h) and daily (≥24 h) period after PM_2.5 exposure might be sensitive time window to detect the responses of salivary (i.e. CRP, TNF) and urinary biomarkers (i.e. 8-iso-PGF_2α), respectively.

Short-Term Effects of Particle Size and Constituents on Blood Pressure in Healthy Young Adults in Guangzhou, China

Background Although several studies have focused on the associations between particle size and constituents and blood pressure, results have been inconsistent. Methods and Results We conducted a panel study, between December 2017 and January 2018, in 88 healthy university students in Guangzhou, China. Weekly systolic blood pressure and diastolic blood pressure were measured for each participant for 5 consecutive weeks, resulting in a total of 440 visits. Mass concentrations of particles with an aerodynamic diameter of ≤2.5 µm (PM2.5), ≤1.0 µm (PM1.0), ≤0.5 µm (PM0.5), ≤0.2 µm (PM0.2), and number concentrations of airborne particulates of diameter ≤0.1 μm were measured. Linear mixed-effect models were used to estimate the associations between blood pressure and particles and PM2.5 constituents 0 to 48 hours before blood pressure measurement. PM of all the fractions in the 0.2- to 2.5-μm range were positively associated with systolic blood pressure in the first 24 hours, with the percent changes of effect estimates ranging from 3.5% to 8.8% for an interquartile range increment of PM. PM0.2 was also positively associated with diastolic blood pressure, with an increase of 5.9% (95% CI, 1.0%-11.0%) for an interquartile range increment (5.8 μg/m3) at lag 0 to 24 hours. For PM2.5 constituents, we found positive associations between chloride and diastolic blood pressure (1.7% [95% CI, 0.1%-3.3%]), and negative associations between vanadium and diastolic blood pressure (-1.6% [95% CI, -3.0% to -0.1%]). Conclusions Both particle size and constituent exposure are significantly associated with blood pressure in the first 24 hours following exposure in healthy Chinese adults.

An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments

Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM_2.5; fine particles) and ≤0.1 μm (PM_0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM_2.5 and PM_0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides guidance to prepare and plan efficient sampling campaigns to collect sufficient PM mass for various purposes in a reasonable timeframe.

Health effects of occupational exposure to printer emissions on workers in China: Cardiopulmonary function change

Printers emitted nanoparticles (NPs), ozone (O₃) and volatile organic chemicals (VOCs) during operation that elicited adverse effects on indoor air quality of the printing room, which may affect the health of exposed workers. The aim of this work was to explore the health effects of occupational exposure to printer emissions on workers, especially cardiovascular and lung function. We sampled particles in the print shop for characterization, including particle size distribution and elemental composition, and measured PM₁ number concentrations in print shops and other workplaces. We assessed blood pressure, heart rate and pulmonary function in 53 printing room workers and 54 controls in Beijing, China. Multiple linear regression analysis were used to examine health effects of exposure to printer emissions. The PM₁ number concentration in the print shop was more than 2 times that of the control group. Compared with controls, the exposed workers with lower education and income had heavier workload with a median of 7 days per week and 12 h per day on working days, and presented cardiopulmonary function injury with increased the diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP). The most significant changes of cardiopulmonary function were found in exposed workers with more than 10 years of working age. Multiple linear regression also showed printer emissions exposure was associated with increased SBP and MAP, while decreased lung function indices. This study found changes in the cardiopulmonary function of staff members exposed to printer emissions, which prompted the necessity and urgency of improving the environment of printing rooms and protecting the health of exposed workers.

The association between short-term exposure to extremely high level of ambient fine particulate matter and blood pressure: a panel study in Beijing, China

High blood pressure (BP) is known as the main determinant of high cerebrovascular disease levels in China. Many studies discovered the associations between short-term exposure to PM_2.5 and BP, while most of those focused on low or medium PM_2.5 concentration. The aim of this study was to reveal the association between extremely high level ambient PM_2.5 exposure and BP. We conducted a repeated-measures panel study in Beijing, China, during December 1, 2016 to December 28, 2016. BP was monitored daily for all 133 participants. Daily concentration of PM_2.5 was obtained from local monitoring sites. A linear mixed-effect model combined with the distributed lag non-linear model was used to evaluate the associations between PM_2.5 and daily variations in BP. This study showed short-term exposure to PM_2.5 that was significantly associated with increased DBP (on lags of 0-8 days, Beta = 0.12, 95% confidence interval 0.04, 0.20). The single day effect of PM_2.5 on DBP had a 2-day lag, and the cumulative effect lags 5 days. The effects of PM_2.5 on SBP and DBP on hypertensive adults were significant. The cumulative effect of PM_2.5 on SBP and DBP had 2 rapidly increasing periods in hypertensive adults: lags of 0-2 days and lags of 0-7 days to lags of 0-11 days. Our study revealed that short-term exposure in the extreme high level of ambient PM_2.5 may increase BP among adults. Hypertensive adults may more sensitive than normotensive adults. The periodic high concentration of ambient PM_2.5 might magnify the effect of PM_2.5 on BP increase.