Daily land use regression estimated woodsmoke and traffic pollution concentrations and the triggering of ST-elevation myocardial infarction: a case-crossover study

Geospatial analysis of short-term exposure to air pollution and risk of cardiovascular diseases and mortality-A systematic review

The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk. Four databases were investigated to select publications between 2018 and 2023 that met the inclusion criteria of studying the effect of particulate matter (PM2.5 and PM10), SO2, NOx, CO, and O3 on CVD mortality or morbidity, utilizing pollution modelling techniques, and considering spatial and temporal confounders. Out of 3277 publications, 285 were identified for full-text review, of which 34 satisfied the inclusion criteria for qualitative analysis, and 12 of them were chosen for additional quantitative analysis. Quality assessment revealed that 28 out of 34 included articles scored 4 or above, indicating high quality. In 30 studies, advanced pollution modelling techniques were used, while in 4 only simpler methods were applied. The most pertinent confounders identified were socio-demographic variables (e.g., socio-economic status, population percentage by race or ethnicity) and neighbourhood-level built environment variables (e.g., urban/rural area, percentage of green space, proximity to healthcare), which exhibited varying modifier effects depending on the context. In the quantitative analysis, only PM 2.5 showed a significant positive association to all-cause CVD-related hospitalisation. Other pollutants did not show any significant effect, likely due to the high inter-study heterogeneity and a limited number of cases. The application of advanced geospatial measurement and modelling of air pollution exposure, as well as its risk, is increasing. This review underscores the importance of accounting for unconventional neighbourhood-level confounders to enhance the understanding of the CVD risk associated with short-term pollution exposure.

Associations between hourly ambient particulate matter air pollution and ambulance emergency calls among 3,022,164 patients: time stratified case-crossover study

BACKGROUND

Associations between short-term exposure to ambient particulate matter (PM) air pollutants and mortality or hospital admissions have been well documented in previous studies. Less is known about the associations of hourly exposure to PM air pollutants with ambulance emergency calls (AECs) for all causes and specific causes by conducting a case-crossover study. In addition, different patterns of AECs may be attributed to different seasons and daytime/nighttime periods.

OBJECTIVE

In this study, we quantified the risk of all-cause and cause-specific AECs associated with hourly PM air pollutants between 1 January 2013 and 31 December 2019 in Shenzhen, China. We also examined whether the observed associations of particulate matter air pollutants with AECs for all causes differed across strata defined by sex, age, season, and time of day.

METHODS

We used ambulance emergency dispatch data and environmental data between 1 January 2013 and 31 December 2019 from the Shenzhen Ambulance Emergency Centre and the National Environmental Monitor Station to conduct a time-stratified case-crossover study design to estimate the associations of air pollutants (i.e., PM2.5, PM10) with all-cause AECs and cause-specific AECs. We generated a well-established distributed lag nonlinear model for nonlinear concentration response and nonlinear lag response functions. We used conditional logistic regression to estimate odds ratios (ORs) with 95% confidence intervals (CIs), adjusted for public holidays, season, time of day, day of the week, hourly temperature, and hourly humidity to examine the association of all-cause and cause-specific AECs with hourly air pollutant concentrations.

RESULTS

A total of 3,022,164 patients were identified during the study period in Shenzhen. Each IQR increase in PM2.5 (24.0 µg/m3) and PM10 (34.0 µg/m3) concentrations in 24 hours was associated with an increased risk of AECs (PM2.5: all-cause, 1.8%, 95% CI, 0.8%-2.4%; PM10: all-cause, 2.0%, 95% CI, 1.1%-2.9%). We observed a stronger association of all-cause AECs with PM2.5 and PM10 in the daytime than in the nighttime and in the elderly group than in the younger group (PM2.5 daytime, 1.7%, 95% CI, 0.5%-3.0%; nighttime, 1.4%, 95% CI, 0.3%-2.6%; PM10 daytime, 2.1%, 95% CI, 0.9%-3.4%; nighttime, 1.7%, 95% CI, 0.6%-2.8%; PM2.5 18-64 years, 1.4%, 95% CI, 0.6%-2.1%; ≥65 years, 1.6%, 95% CI, 0.6%-2.6%; PM10 18-64 years, 1.8%, 95% CI, 0.9%-2.6%; ≥65 years, 2.0%, 95% CI, 1.1%-3.0%).

CONCLUSIONS

The risk of all-cause AECs increased consistently with increasing concentrations of PM air pollutants, showing a nearly linear relationship with no apparent thresholds. Particulate matter air pollution increase was associated with a higher risk of AECs for all causes, cardiovascular, respiratory, and reproductive AECs. The results of this study may be valuable to air pollution attributable to the distribution of emergency resources, and consistent air pollution control.

CLINICALTRIAL

Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site

INTRODUCTION

Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development.

The key pollutants that are the key focus of this work include nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM2.5; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O3). NOx, CO, and BC are tracers of vehicle emissions and dispersion. PM2.5 is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM2.5 and UFP concentrations. O3 concentrations are influenced by interaction with NOx near the roadway. Nitrogen dioxide (NO2), CO, PM2.5, and O3 are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work.

METHODS

The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection.

The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM2.5 at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites.

RESULTS

We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system.

At the freeway site, we measured the spatial gradient of NOx, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants.

At the urban site, we measured daily average concentrations of nitric oxide (NO), NOx, O3, and PM2.5 at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O3, PM2.5, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution.

Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R2) was 0.61 for hourly NOx concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NOx at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NOx at the freeway site and for UFPs and O3 at the urban site pedestrian transect.

CONCLUSIONS

Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.

Characterization of in-stack particulate emissions from residential wood hydronic heater appliances under different combustion conditions

In the current work, we provide measurements of size-resolved particle number concentration (PNC), particle mass concentration (PMC), lung-deposited surface area (LDSA), and black carbon (BC) concentration for three biomass fired hydronic heaters during operation in four different combustion conditions. The appliances include one woodchip-fueled hydronic heater and two outdoor cordwood-fueled hydronic heaters. The operating conditions included startup, low output, high output, and burnout. Measurements were made using a custom dilution sampling system and a suite of commercially available, time-resolved, ambient aerosol measurement instrumentation. The PNC, as measured using an Dekati Electrical Low Pressure Impactor+ (ELPI), had operating condition mean values ranging between 4.1 and 52 million particles per cubic centimeter (#/cm³). The highest reported PNC occurred during the startup condition in all cases. Calculating the particle size distribution measured across each operating phase for the same instrument gave geometric mean diameters (d_g) in the range of 0.080-0.256 µm. The largest d_g per appliance was nearly always attributable to the startup condition (for hydronic heater 1, startup d_g ranked second).We did not observe the same trends when we transformed the ELPI PNC to PMC and particle surface area concentration estimates across operating conditions, suggesting PNC and d_g are highly variable. Furthermore, simultaneous measurements of PNC, PMC, and PSAC using instrumentation with different working principles gave varying results, potentially suggesting that particles of different composition and morphology are produced under different combustion conditions.Implications: In this work we compare the results from testing of 3 biomass fired hydronic heaters including one chip-fired appliance and two cordwood-fired appliances. The emissions from these appliances were made across four operating conditions and using three different non-regulatory emissions metrics. This work: describes the difference between chip and cordwood fired units and the effect of operating condition on emissions across the three emissions metrics.

Quantifying spatial heterogeneity of vulnerability to short-term PM2.5 exposure with data fusion framework

The current estimations of the burden of disease (BD) of PM_2.5 exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM_2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM_2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006-2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM_2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20-1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM_2.5 exposure data (20-32%) and risk values (0-86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.

Assessing the Impact of Wildfires on the Use of Black Carbon as an Indicator of Traffic Exposures in Environmental Epidemiology Studies

Epidemiological studies frequently use black carbon (BC) as a proxy for traffic-related air pollution (TRAP). However, wildfire smoke (WFS) represents an important source of BC not often considered when using BC as a proxy for TRAP. Here, we examined the potential for WFS to bias TRAP exposure assessments based on BC measurements. Weekly integrated BC samples were collected across the Denver, CO region from May to November 2018. We collected 609 filters during our sampling campaigns, 35% of which were WFS-impacted. For each filter we calculated an average BC concentration. We assessed three GIS-based indicators of TRAP for each sampling location: annual average daily traffic within a 300 m buffer, the minimum distance to a highway, and the sum of the lengths of roadways within 300 m. Median BC concentrations were 9% higher for WFS-impacted filters (median = 1.14 μg/m3, IQR = 0.23 μg/m3) than nonimpacted filters (median = 1.04 μg/m3, IQR = 0.48 μg/m3). During WFS events, BC concentrations were elevated and expected spatial gradients in BC were reduced. We conducted a simulation study to estimate TRAP exposure misclassification as the result of regional WFS. Our results suggest that linear health effect estimates were biased away from the null when WFS was present. Thus, exposure assessments relying on BC as a proxy for TRAP may be biased by wildfire events. Alternative metrics that account for the influence of "brown" carbon associated with biomass burning may better isolate the effects of traffic emissions from those of other black carbon sources.

Cardiovascular health impacts of wildfire smoke exposure

In recent years, wildland fires have occurred more frequently and with increased intensity in many fire-prone areas. In addition to the direct life and economic losses attributable to wildfires, the emitted smoke is a major contributor to ambient air pollution, leading to significant public health impacts. Wildfire smoke is a complex mixture of particulate matter (PM), gases such as carbon monoxide, nitrogen oxide, and volatile and semi-volatile organic compounds. PM from wildfire smoke has a high content of elemental carbon and organic carbon, with lesser amounts of metal compounds. Epidemiological studies have consistently found an association between exposure to wildfire smoke (typically monitored as the PM concentration) and increased respiratory morbidity and mortality. However, previous reviews of the health effects of wildfire smoke exposure have not established a conclusive link between wildfire smoke exposure and adverse cardiovascular effects. In this review, we systematically evaluate published epidemiological observations, controlled clinical exposure studies, and toxicological studies focusing on evidence of wildfire smoke exposure and cardiovascular effects, and identify knowledge gaps. Improving exposure assessment and identifying sensitive cardiovascular endpoints will serve to better understand the association between exposure to wildfire smoke and cardiovascular effects and the mechanisms involved. Similarly, filling the knowledge gaps identified in this review will better define adverse cardiovascular health effects of exposure to wildfire smoke, thus informing risk assessments and potentially leading to the development of targeted interventional strategies to mitigate the health impacts of wildfire smoke.

Use of low-cost PM monitors and a multi-wavelength aethalometer to characterize PM2.5 in the Yakama Nation Reservation

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 micrometers (PM_2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM_2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM_2.5 >12 μg/m³ occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m³), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375-880nm, were: summer - wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer - no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2-1.8), summer - wildfire smoke 1.4 (1.0-1.8), fall 1.3 (1.1-1.4), spring 1.2 (1.1-1.4), and summer - no wildfire smoke 1.2 (1.0-1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM_2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further understanding of how to interpret aethalometer data in regions with complex emissions would contribute to much-needed research in communities impacted by air pollution from agricultural as well as residential sources of combustion.

Weather effects on hand, foot, and mouth disease at individual level: a case-crossover study

BACKGROUND

Hand, foot, and mouth disease (HFMD) raises an urgent public health issue in the Asia-Pacific region, especially in China. The associations between weather factors and HFMD have been widely studied but with inconsistent results. Moreover, previous studies utilizing ecological design could not rule out the bias of exposure misclassification and unobserved confounders.

METHODS

We used case-crossover analysis to assess the associations of weather factors on HFMD. Individual HFMD cases from 2009 to 2012 in Guangdong were collected and cases located within 10 km of the meteorological monitoring sites were included. Lag effects were examined through the previous 7 days. In addition, we explored the variability by changing the distance within 20 km and 30 km.

RESULTS

We observed associations between HFMD and weather factors, including temperature and relative humidity. An approximately U-shaped relationship was observed for the associations of temperature on HFMD across the same day and the previous 7 days, while an approximately exponential-shaped was seen for relative humidity. Statistically significant increases in rates of HFMD were associated with each 10-unit increases in temperature [Excess rate (ER): 7.7%; 95% Confidence Interval (CI): 3.9, 11.7%] and relative humidity (ER: 1.9%; 95% CI: 0.7, 3.0%) on lag days 0-6, when assessing within 10 km of the monitoring sites. Potential thresholds for temperature (30.0 °C) and relative humidity (70.3%) detected showed associations with HFMD. The associations remained robust for 20 km and 30 km.

CONCLUSIONS

Our study found that temperature and relative humidity are significantly associated with the increased rates of HFMD. Thresholds and lag effects were observed between weather factors and HFMD. Our findings are useful for planning on targeted prevention and control of HFMD.

Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York State

BACKGROUND

Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference.

OBJECTIVES

To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations.

METHODS

Using source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days.

RESULTS

Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources.

CONCLUSIONS

These findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events.

Association between ambient particulate matter air pollution and ST-elevation myocardial infarction: A case-crossover study in a Chinese city

BACKGROUND

Abundant epidemiological studies have revealed that short-term exposure to ambient air pollution increased the incidence of ischemic heart diseases. However, few investigations have explored the association between air pollution and ST-elevation myocardial infarction (STEMI), one major subtype of such events.

METHODS

We conducted a time-stratified case-crossover study in two major hospitals of Yancheng, a city in East China, from January 2015 to February 2018. We used conditional logistic regression models to explore the association between hourly concentrations of air pollutants and STEMI hospitalizations. We explored potential effect modification in susceptible subgroups by age, gender, smoking status, and comorbidities. Two-pollutant models were fitted to test the robustness of the association.

RESULTS

We identified a total of 347 STEMI patients. In single-pollutant models, each 10 μg/m³ increase in concentrations of fine and inhalable particulate matter (PM) (lag 13-24 h) was associated with increments of 5.27% [95% confidence interval (CI): 1.09%, 9.46%] and 3.86% (95%CI: 0.83%, 6.88%) in STEMI hospitalizations, respectively. We observed slightly larger associations of STEMI hospitalization with PM in patients who were older than 65, female, non-smoker, and with comorbidities (hypertension, diabetes or hyperlipidemia). The associations were generally robust to adjustment of criteria gaseous pollutants except for carbon monoxide.

CONCLUSION

This is the first study in China that suggested acute exposure to elevated PM concentrations may trigger STEMI. Patients with cardiometabolic comorbidities were slightly more susceptible to air pollution.

Ambient wintertime particulate air pollution and hypertensive disorders of pregnancy in Monroe County, New York

BACKGROUND

Previous studies have reported associations between ambient fine particle (PM_2.5) concentrations and hypertensive disorders of pregnancy (HDP). However, none have examined whether ultrafine particles (UFP; < 100 nm), accumulation mode particles (AMP; 100-500 nm), markers of traffic pollution (black carbon; BC), or wood burning (Delta-C; (30% of ambient wintertime PM_2.5 in Monroe County, NY is from wood burning)) are associated with an increased odds of HDP. We estimated the odds of HDP associated with increased concentrations of PM_2.5, UFP, AMP, BC, and Delta-C in each gestational month during winter months.

METHODS

Electronic medical records and birth certificate data were linked with land-use regression models in Monroe County, New York in 2009-2013 to predict monthly pollutant concentrations during winter (November-April) based on maternal residential address for 16,637 births. Using multivariable logistic regression, we estimated the odds of HDP associated with each interquartile range (IQR) increase in PM_2.5, UFP, AMP, BC, and Delta-C concentrations during each gestational month, adjusting for maternal characteristics, birth hospital, temperature, and relative humidity.

RESULTS

Each 0.52 µg/m³ increase in Delta-C concentration during the 7th gestational month was associated with an increased odds of HDP (odds ratio (OR) = 1.21; 95% confidence interval (CI) = 1.01, 1.45), with a similar sized estimate in month 8 (OR = 1.18; 95%CI = 0.98, 1.43). Non-statistically significant increased odds of HDP associated with IQR increases in BC concentrations during months 3 (OR = 1.12; 95%CI = 0.98, 1.28) and 7 (OR = 1.12; 95%CI = 0.96, 1.29) were observed. Increased odds of HDP were not observed for PM_2.5, UFP, or AMP.

CONCLUSIONS

Our findings suggest that maternal exposure to wood smoke in Monroe County during winter is associated with an increased odds of HDP during late gestation. Additional studies are needed to evaluate the effect of wood smoke on HDP and to explore effects on other pregnancy outcomes.

Hourly land-use regression models based on low-cost PM monitor data

Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.

Triggering of cardiovascular hospital admissions by fine particle concentrations in New York state: Before, during, and after implementation of multiple environmental policies and a recession

BACKGROUND

Previous studies reported triggering of acute cardiovascular events by short-term increasedPM_2.5 concentrations. From 2007 to 2013, national and New York state air quality policies and economic influences resulted in reduced concentrations of PM_2.5 and other pollutants across the state. We estimated the rate of cardiovascular hospital admissions associated with increased PM_2.5 concentrations in the previous 1-7 days, and evaluated whether they differed before (2005-2007), during (2008-2013), and after these concentration changes (2014-2016).

METHODS

Using the Statewide Planning and Research Cooperative System (SPARCS) database, we retained all hospital admissions with a primary diagnosis of nine cardiovascular disease (CVD) subtypes, for residents living within 15 miles of PM_2.5 monitoring sites in Buffalo, Rochester, Albany, Queens, Bronx, and Manhattan from 2005 to 2016 (N = 1,922,918). We used a case-crossover design and conditional logistic regression to estimate the admission rate for total CVD, and nine specific subtypes, associated with increased PM_2.5 concentrations.

RESULTS

Interquartile range (IQR) increases in PM_2.5 on the same and previous 6 days were associated with 0.6%-1.2% increases in CVD admission rate (2005-2016). There were similar patterns for cardiac arrhythmia, ischemic stroke, congestive heart failure, ischemic heart disease (IHD), and myocardial infarction (MI). Ambient PM_2.5 concentrations and annual total CVD admission rates decreased across the period. However, the excess rate of IHD admissions associated with each IQR increase in PM_2.5 in previous 2 days was larger in the after period (2.8%; 95%CI = 1.5%-4.0%) than in the during (0.6%; 95%CI = 0.0%-1.2%) or before periods (0.8%; 95%CI = 0.2%-1.3%), with similar patterns for total CVD and MI, but not other subtypes.

CONCLUSIONS

While pollutant concentrations and CVD admission rates decreased after emission changes, the same PM_2.5 mass was associated with a higher rate of ischemic heart disease events. Future work should confirm these findings in another population, and investigate whether specific PM components and/or sources trigger IHD events.