Air pollution at Rochester, NY: Long-term trends and multivariate analysis of upwind SO2 source impacts

Decomposing PM2.5 concentrations in urban environments into meaningful factors: 1. Separating the contribution of local anthropogenic activities from background and long-range transport

Fine particulate matter (PM2.5) is a complex mixture of aerosol particles with varying properties and sources, both local and distant. In areas lacking detailed monitoring of PM2.5 speciation, the common source-apportionment analyses are not applicable. This study demonstrates an alternative framework for estimating sources and processes that affect observed PM2.5 concentrations when information on the particle composition is unavailable. Eight years (2012-2019) of half-hourly PM2.5 observations from 10 air quality monitoring (AQM) stations, clustered according to their airmass transport sector were analyzed, using Non-negative Matrix Factorization (NMF). Factors were determined based on their variation in time, space, and between airmass sectors. Employing a supervised machine-learning model provided insights into the relationships between the extracted factors, meteorological parameters and co-measured airborne pollutants. Factor interpretations were evaluated through comparisons with measurements of PM2.5 species from a nearby Surface PARTiculate mAtter Network (SPARTAN) station. The NMF successfully separated background factors from an urban anthropogenic-activity factor, with the latter accounting for approximately 60 % of the observed PM2.5 levels in Tel Aviv (∼10±6μg/m3). Positive monotonic relationships were observed between the PM2.5 urban anthropogenic-activity factor and measurements of nitrogen oxides (NOx) and absolute humidity (AH), representing the impact of traffic emissions and hygroscopic growth, respectively. The summer background factor was found to represent long-range transport (LRT) from Europe, showing a good agreement (R2 = 0.81) with ammonium sulphate concentrations. Our results demonstrate that a spatial NMF analysis can reliably estimate contributions of different sources with distinct compositions and properties to the total observed PM2.5. Using such an analysis, future environmental health studies could assess health risks associated with exposure to distinct PM2.5 fractions. This information may assist decision makers to set environmental targets for abating PM2.5 with specific compositions and properties.

Trends of inorganic sulfur and nitrogen species at an urban site in western Canada (2004-2018)

A 15-year (2004-2018) record of measurement data of gaseous and particulate sulfur and nitrogen pollutants in air collected at an urban site in Burnaby in western Canada was analyzed for generating their decadal trends using three different methods, including linear regression, Mann-Kendall test and Theil-Sen trend estimator (MK-TS), and ensemble empirical mode decomposition (EEMD). Annual mean concentration of SO2 and SO42- decreased by about 59% and 42%, respectively, during the 15-year period. The slower decreases of SO42- than SO2 were mainly caused by the increased O3 concentration and temperature in spring and summer, which promoted conversion of SO2 to SO42- through gas-phase reaction, and by the increased aerosol pH value and availability of H2O2 in winter, which enhanced aqueous-phase SO42- formation. Accordingly, the sulfur oxidation ratio (SOR) increased by 23% or more in spring, summer, and winter during the 15-year period. Annual mean concentrations of NO2 and NO3- declined by 36% and 38%, respectively, during this period. On seasonal basis, NO3- decreased faster than NO2 in autumn and slower in winter. The non-linear responses of NO3- to NO2 concentration decreases were more evident in winter than the other seasons, partly due to the increased particulate NO3- fraction caused by decreased temperature, increased aerosol pH value, and enhanced NO3- formation caused by increased O3 concentrations. Annual mean concentration of NH3 showed small increases due to stable NH3 emission and reduced conversion of NH3 to NH4+. NH4+ concentration decreased by 51% during the 15-year period. These results suggest that reduced oxidants levels are likely responsible for weakened formation of secondary inorganic aerosols, besides emission reductions for SO2 and NO2.

Inequitable Exposures to U.S. Coal Power Plant-Related PM2.5: 22 Years and Counting

BACKGROUND

Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups.

OBJECTIVES

We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤2.5μm (PM2.5) associated with coal power plant SO2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions.

METHODS

We developed a data set of annual PM2.5 source impacts ("coal PM2.5") associated with SO2 emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups.

RESULTS

Nationwide population-weighted coal PM2.5 declined from 1.96μg/m3 in 1999 to 0.06 μg/m3 in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West.

DISCUSSION

We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM2.5. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM2.5 associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

Long-term trends of ultrafine and fine particle number concentrations in New York State: Apportioning between emissions and dispersion

In the past several decades, a variety of efforts have been made in the United States to improve air quality, and ambient particulate matter (PM) concentrations have been used as a metric to evaluate the efficacy of environmental policies. However, ambient PM concentrations result from a combination of source emission rates and meteorological conditions, which also change over time. Dispersion normalization was recently developed to reduce the influence of atmospheric dispersion and proved an effective approach that enhanced diel/seasonal patterns and thus provides improved source apportionment results for speciated PM mass and particle number concentration (PNC) measurements. In this work, dispersion normalization was incorporated in long-term trend analysis of 11-500 nm PNCs derived from particle number size distributions (PNSDs) measured in Rochester, NY from 2005 to 2019. Before dispersion normalization, a consistent reduction was observed across the measured size range during 2005-2012, while after 2012, the decreasing trends slowed down for accumulation mode PNCs (100-500 nm) and reversed for ultrafine particles (UFPs, 11-100 nm). Through dispersion normalization, we showed that these changes were driven by both emission rates and dispersion. Thus, it is important for future studies to assess the effects of the changing meteorological conditions when evaluating policy effectiveness on controlling PM concentrations. Before and after dispersion normalization, an evident increase in nucleation mode particles was observed during 2015-2019. This increase was possibly enabled by a cleaner atmosphere and will pose new challenges for future source apportionment and accountability studies.

Identification of decadal trends and associated causes for organic and elemental carbon in PM2.5 at Canadian urban sites

Chemically resolved data for fine particulate matter (PM_2.5) have been collected across Canada since 2003 through the National Air Pollution Surveillance (NAPS) network. Seven urban sites that have 10-17 years (2003-2019) of PM_2.5 organic carbon (OC) and elemental carbon (EC) data were selected for analysis of decadal trends of OC, EC, and OC/EC ratio using the Ensemble Empirical Mode Decomposition method. Results showed that OC and EC decreased by 0.009-0.072 μg m^-3 yr^-1 and 0.028-0.049 μg m^-3 yr^-1, or 0.77-3.1 % yr^-1 and 3.2-6.7 % yr^-1, respectively, depending on the location. The more rapid decrease in EC than OC resulted in an increasing trend in the OC/EC ratio of 0.03-0.19 yr^-1 across the sites. Macro-tracer approach was used to estimate source attributions of OC and EC from wood burning, fossil fuel combustion, and secondary aerosol formation. Using this approach, it was identified that the significant decrease in EC during the past decade was predominately caused by reduced on-road emissions. The decreased emissions from wood burning and transportation dominated the decline of OC, but such a decline was largely offset by the enhanced secondary organic aerosol (SOA) formation, resulting in much weaker decline of OC than EC. The enhanced SOA formation was due to the increased biogenic emissions fully offsetting the decreased anthropogenic emissions for volatile organic compounds. These findings highlight the need for quantifying biogenic sources of VOCs and other oxidants that are involved in OC formation at the national scale.

Compositional changes of PM2.5 in NE Spain during 2009-2018: A trend analysis of the chemical composition and source apportionment

In this work, time-series analyses of the chemical composition and source contributions of PM_2.5 from an urban background station in Barcelona (BCN) and a rural background station in Montseny (MSY) in northeastern Spain from 2009 to 2018 were investigated and compared. A multisite positive matrix factorization analysis was used to compare the source contributions between the two stations, while the trends for both the chemical species and source contributions were studied using the Theil-Sen trend estimator. Between 2009 and 2018, both stations showed a statistically significant decrease in PM_2.5 concentrations, which was driven by the downward trends of levels of chemical species and anthropogenic source contributions, mainly from heavy oil combustion, mixed combustion, industry, and secondary sulfate. These source contributions showed a continuous decrease over the study period, signifying the continuing success of mitigation strategies, although the trends of heavy oil combustion and secondary sulfate have flattened since 2016. Secondary nitrate also followed a significant decreasing trend in BCN, while secondary organic aerosols (SOA) very slightly decreased in MSY. The observed decreasing trends, in combination with the absence of a trend for the organic aerosols (OA) at both stations, resulted in an increase in the relative proportion of OA in PM_2.5 by 12% in BCN and 9% in MSY, mostly from SOA, which increased by 7% in BCN and 4% in MSY. Thus, at the end of the study period, OA accounted for 40% and 50% of the annual mean PM_2.5 at BCN and MSY, respectively. This might have relevant implications for air quality policies aiming at abating PM_2.5 in the study region and for possible changes in toxicity of PM_2.5 due to marked changes in composition and source apportionment.

Arsenic and SO2 hotspot in South-Eastern Europe: An overview of the air quality after the implementation of the flash smelting technology for copper production

This extensive study considered the air pollution data after the flash smelting technology for copper production had become fully operational. The assessment of the air quality after the implementation was significantly important, since the modernisation was necessary for reducing the environmental contamination in one of the most polluted regions in South-Eastern Europe. The concentrations of SO₂, PM₁₀ and toxic elements (As, Pb, Cd, Ni) in PM₁₀ samples were monitored at different sites, with respect to the copper smelter, in the period 2016-2019. The air quality evaluation was performed concerning the corresponding limit and target values defined by the Serbian and European legislation, as well as the World Health Organization Air Quality Guidelines (WHO AQG). The measured SO₂ concentrations indicated frequent exceedances of the defined daily and annual limit values, at both national and European level. Although exceedances were not as pronounced as in the period before the implementation of the new technology, the episodes of extreme air pollution with SO₂ persisted on the daily basis. The maximum daily SO₂ concentration of 2125 μg m^-3 was more than 100 times higher compared to the WHO AQG, but lower compared to the period before the implementation of the flash smelting technology. The air quality considering PM₁₀ and especially As levels in PM₁₀ samples was notably poorer after the modernisation. The annual target value for As, defined by the European and Serbian Regulation, was exceeded at all the measuring sites, with maximum exceedance of more than 90 times at the suburban site during 2019. The frequent exceedances of the corresponding annual limit and target values were also denoted for Pb and Cd in PM₁₀ samples. The analysed data emphasised that the Bor area could still be characterised as an environmental hotspot in Serbia and beyond.

Environmental behavior and potential driving force of bisphenol A in the Elbe River: A long-term trend study

As an endocrine disruptor, a deep understanding of the environmental behavior and potential driving force of bisphenol A (BPA) is helpful for developing a mitigation strategy and reducing the exposure risk to the public. Based on long-term monitoring data from 2004 to 2016, this study systematically evaluated the long-term trend, periodic characteristics, and potential risks of BPA in the Elbe River in the state of Saxony, Germany. Multiple advanced statistical approaches were employed for data mining. Pettitt's test was used to determine the main change points of BPA that occurred from 2008 to 2011. The Mann-Kendall test showed a decreasing trend in BPA concentrations (slope: -0.087 to -0.112, P < 0.05) over the past 13 years, particularly in the wet seasons (slope: -0.730 to -0.038, P < 0.05). Wavelet analysis revealed similar periodicities of BPA among stations (which experienced 4-5 oscillations in the first major period). The ARIMA model forecasted the mean BPA concentration as ranging from 9 to 41 ng L^-1 in the subsequent 3 months, which was similar to that in the last 3 months (20-42 ng L^-1). Besides, the highest hazard quotients (>0.3) were documented for Chironomus riparius, Oryzias latipes, Potamopyrgus antipodarum, and Hydra vulgar, which indicates that BPA may threaten their growth and development. The hazard index values for non-cancer risk of BPA no greater than 6.47 × 10^-9 (HQ far below 1), which suggests that BPA did not pose a significant threat to human health. Because BPA pollution is closely related to industrial activities, a long-term decline in BPA concentrations could be attributed to the reduced number of factories, limited discharge, and improved decontamination efficiency. However, the minimal change in the BPA concentration in the near future could reflect periodic fluctuations.

Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

Application of spatial analysis to investigate contribution of VOCs to photochemical ozone creation

This study was concerned with the temporal analysis of benzene, toluene, ethylbenzene, xylenes (BTEXs), and ozone in Rochester, New York, between 2012 and 2018. Additionally, the influence of ozone precursors (e.g., BTEXs and NO₂) and meteorological variables (e.g., relative humidity (RH), temperature along with wind speed) on ozone dispersion was investigated in the eastern half of the USA using the integrated nested Laplace approximation and stochastic partial differential equation (INLA-SPDE). The benzene variability at seasonal scale was characterized by higher values during the cold seasons. On the contrary, the long-term temporal trend of ozone depicted a repetitive cyclic behavior while an episode, with values exceeding 5 μg/m³, was detected associated with benzene in 2015. The spatial analysis by INLA-SPDE indicated that 1,3,5-trimethylbenzene and benzene were the key ozone precursors influencing ozone formation. It was demonstrated that increase of temperature had a considerable impact on ozone build-up whereas the increment of RH leads to decrease in ambient values of ozone. The amounts of root mean squared error (RMSE), mean absolute error (MAE), and bias for the validation data (e.g., 32 samples) were 0.005, 0.004, and 0.0008, exhibiting a reasonable out-of-sample forecasting by the INLA-SPDE model. The distribution map of ozone highlighted a hot spot in the state of Florida.

An overview of selected emerging outdoor airborne pollutants and air quality issues: The need to reduce uncertainty about environmental and human impacts

According to the literature, it is estimated that outdoor air pollution is responsible for the premature death in a range from 3.7 to 8.9 million persons on an annual basis across the world. Although there is uncertainty on this figure, outdoor air pollution represents one of the greatest global risks to human health. In North America, the rapid evolution of technologies (e.g., nanotechnology, unconventional oil and gas rapid development, higher demand for fertilizers in agriculture) and growing demand for ground, marine and air transportation may result in significant increases of emissions of pollutants that have not been carefully studied so far. As a result, these atmospheric pollutants insufficiently addressed by science in Canada and elsewhere are becoming a growing issue with likely human and environmental impacts in the near future. Here, an emerging pollutant is defined as one that meets the following criteria: 1) potential or demonstrated risk for humans or the environment, 2) absence of Canada-wide national standard, 3) insufficient routine monitoring, 4) yearly emissions greater than one ton in Canada, 5) insufficient data concerning significant sources, fate, and detection limit, and 6) insufficiently addressed by epidemiological studies. A new methodology to rank emerging pollutants is proposed here based on weighting multiple criteria. Some selected emerging issues are also discussed here and include the growing concern of ultrafine or nanoparticles, growing ammonia emissions (due to rapid expansion of the agriculture), increased methane/ethane/propane emissions (due to the expanding hydraulic fracturing in the oil and gas sector) and the growing transportation sector. Finally, the interaction between biological and anthropogenic pollution has been found to be a double threat for public health. Here, a multidisciplinary and critical overview of selected emerging pollutants and related critical issues is presented with a focus in Canada.Implications: This overview paper provides a selection methodology for emerging pollutants in the atmospheric environment. It also provides a critical discussion of some related issues. The ultimate objective is to inform about the need to 1) address emerging issues through adequate surface monitoring and modeling in order to inform the development of regulations, 2) reduce uncertainties by geographically mapping emerging pollutants (e.g., through data fusion, data assimilation of observations into air quality models) which can improve the scientific support of epidemiological studies and policies. This review also highlights some of the difficulties with the management of these emerging pollutants, and the need for an integrated approach.

The Impacts of Different Air Pollutants on Domestic and Inbound Tourism in China

Previous studies have reported that air pollution negatively affects the tourism industry. This paper attempted to answer the following question: among different air pollutants, which one acts as the most adverse factor? The study was based on a sample of panel data covering 337 Chinese cities for the period between 2007 and 2016. Four pollutant indicators were inspected: PM 2 . 5 (particulate matter 2.5 micrometers or less in size), PM 10 (particulate matter 10 micrometers or less in size), SO 2 (sulfur dioxide), and NO 2 (nitrogen dioxide). It was found that PM 2 . 5 had a significantly negative impact on both domestic and inbound tourist arrivals. Regarding the other three pollutant indicators, except for the negative influence of NO 2 on inbound tourist arrivals, no statistically significant impact was found. This study suggests that tourism policy makers should primarily focus on PM 2 . 5 , when considering the nexus between air quality and tourism development. According to our estimates, the negative impact of PM 2 . 5 on tourism is substantial. If the PM 2 . 5 concentration in the ambient air increases by 1 μ g/m 3 (=0.001 mg/m 3 ), domestic and inbound tourist arrivals will decline by 0.482% and 1.227%, respectively. These numbers imply an average reduction of 81,855 person-times in annual domestic tourist arrivals and 12,269 in inbound tourist arrivals in each city.

Changes in triggering of ST-elevation myocardial infarction by particulate air pollution in Monroe County, New York over time: a case-crossover study

BACKGROUND

Previous studies have reported that fine particle (PM_2.5) concentrations triggered ST elevation myocardial infarctions (STEMI). In Rochester, NY, multiple air quality policies and economic changes/influences from 2008 to 2013 led to decreased concentrations of PM_2.5 and its major constituents (SO₄^2-, NO₃^-, elemental and primary organic carbon). This study examined whether the rate of STEMI associated with increased ambient gaseous and PM component concentrations was different AFTER these air quality policies and economic changes (2014-2016), compared to DURING (2008-2013) and BEFORE these polices and changes (2005-2007).

METHODS

Using 921 STEMIs treated at the University of Rochester Medical Center (2005-2016) and a case-crossover design, we examined whether the rate of STEMI associated with increased PM_2.5, ultrafine particles (UFP, < 100 nm), accumulation mode particles (AMP, 100-500 nm), black carbon, SO₂, CO, and O₃ concentrations in the previous 1-72 h was modified by the time period related to these pollutant source changes (BEFORE, DURING, AFTER).

RESULTS

Each interquartile range (3702 particles/cm³) increase in UFP concentration in the previous 1 h was associated with a 12% (95% CI = 3%, 22%) increase in the rate of STEMI. The effect size was larger in the AFTER period (26%) than the DURING (5%) or BEFORE periods (9%). There were similar patterns for black carbon and SO₂.

CONCLUSIONS

An increased rate of STEMI associated with UFP and other pollutant concentrations was higher in the AFTER period compared to the BEFORE and DURING periods. This may be due to changes in PM composition (e.g. higher secondary organic carbon and particle bound reactive oxygen species) following these air quality policies and economic changes.

Spatial-temporal variations of summertime ozone concentrations across a metropolitan area using a network of low-cost monitors to develop 24 hourly land-use regression models

Ten relatively-low-cost ozone monitors (Aeroqual Series 500 with OZL ozone sensor) were deployed to assess the spatial and temporal variability of ambient ozone concentrations across residential areas in the Monroe County, New York from June to October 2017. The monitors were calibrated in the laboratory and then deployed to a local air quality monitoring site where they were compared to the federal equivalent method values. These correlations were used to correct the measured ozone concentrations. The values were also used to develop hourly land use regression models (LUR) based on the deletion/substitution/addition (D/S/A) algorithm that can be used to predict the spatial and temporal concentrations of ozone at any hour of a summertime day and given location in Monroe County. Adjusted R2 values were high (average 0.83) with the highest adjusted R2 for the model between 8 and 9 AM (i.e. 1-2 h after the peak of primary emissions during the morning rush hours). Spatial predictors with the highest positive effects on ozone estimates were high intensity developed areas, low and medium intensity developed areas, forests + shrubs, average elevation, Interstate + highways, and the annual average vehicular daily traffic counts. These predictors are associated with potential emissions of anthropogenic and biogenic precursors. Maps developed from the models exhibited reasonable spatial and temporal patterns, with low ozone concentrations overnight and the highest concentrations between 11 AM and 5 PM. The adjusted R2 between the model predictions and the measured values varied between 0.79 and 0.87 (mean = 0.83). The combined use of the network of low-cost monitors and LUR modeling provide useful estimates of intraurban ozone variability and exposure estimates that will be used in future epidemiological studies.

Long-Term Changes of Source Apportioned Particle Number Concentrations in a Metropolitan Area of the Northeastern United States

The northeastern United States has experienced significant emissions reductions in the last two decades leading to a decrease in PM2.5, major gaseous pollutants (SO2, CO, NOx) and ultrafine particles (UFPs) concentrations. Emissions controls were implemented for coal-fired power plants, and new heavy-duty diesel trucks were equipped with particle traps and NOx control systems, and ultralow sulfur content is mandatory for both road and non-road diesel as well as residual oil for space heating. At the same time, economic changes also influenced the trends in air pollutants. Investigating the influence of these changes on ultrafine particle sources is fundamental to determine the success of the mitigation strategies and to plan future actions. Particle size distributions have been measured in Rochester, NY since January 2002. The particle sources were investigated with positive matrix factorization (PMF) of the size distributions (11–470 nm) and measured criteria pollutants during five periods (2002–2003, 2004–2007, 2008–2010, 2011–2013, and 2014–2016) and three seasons (winter, summer, and transition). Monthly, weekly, and hourly source contributions patterns were evaluated.

Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Region

The present work reports the distribution of pollutants in the Madrid city and province from 22 monitoring stations during 2010 to 2017. Statistical tools were used to interpret and model air pollution data. The data include the annual average concentrations of nitrogen oxides, ozone, and particulate matter (PM₁₀), collected in Madrid and its suburbs, which is one of the largest metropolitan places in Europe, and its air quality has not been studied sufficiently. A mapping of the distribution of these pollutants was done, in order to reveal the relationship between them and also with the demography of the region. The multivariate analysis employing correlation analysis, principal component analysis (PCA), and cluster analysis (CA) resulted in establishing a correlation between different pollutants. The results obtained allowed classification of different monitoring stations on the basis of each of the four pollutants, revealing information about their sources and mechanisms, visualizing their spatial distribution, and monitoring their levels according to the average annual limits established in the legislation. The elaboration of contour maps by the geostatistical method, ordinary kriging, also supported the interpretation derived from the multivariate analysis demonstrating the levels of NO₂ exceeding the annual limit in the centre, south, and east of the Madrid province.

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.

Long-term trends in submicron particle concentrations in a metropolitan area of the northeastern United States

Significant changes in emission sources have occurred in the northeastern United States over the past decade, due in part to the implementation of emissions standards, the introduction and addition of abatement technologies for road transport, changes in fuel sulfur content for road and non-road transport, as well as economic impacts of a major recession and differential fuel prices. These changes in emission scenarios likely affected the concentrations of airborne submicron particles. This study investigated the characteristics of 11-500nm particle number concentrations and their size spectra in Rochester, NY during the past 15years (2002 to 2016). The modal structure, diurnal, weekly and monthly patterns of particle number concentrations are analyzed. Long-term trends are quantified using seasonal-trend decomposition procedures based on "Loess", Mann-Kendall regression with Theil-Sen slope and piecewise regression. Particle concentrations underwent significant (p<0.05) downward trends. An annual decrease of -323particles/cm³/y (-4.6%/y) was estimated for the total particle number concentration using Theil-Sen analysis. The trends were driven mainly by the decrease in particles in the 11-50nm range (-181particles/cm³/y; -4.7%/y). Slope changes were investigated annually and seasonally. Piecewise regression found different slopes for different portions of the overall period with the strongest declines between 2005 and 2011/2013, followed by small upward trends between 2013 and 2016 for most size bins, possibly representing increased vehicular traffic after the recovery from the 2008 recession.

Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors

There is concern regarding the heterogeneity of exposure to airborne particulate matter (PM) across urban areas leading to negatively biased health effects models. New, low-cost sensors now permit continuous and simultaneous measurements to be made in multiple locations. Measurements of ambient PM were made from October to April 2015-2016 and 2016-2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA. In general, there was moderate spatial inhomogeneity, as indicated by multiple pairwise measures including coefficient of divergence and signed rank tests of the value distributions. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the area, likely driven by a combination of meteorological conditions (wind speed, direction, and mixed layer heights) and the concentration of PM_2.5 being transported into the region. Although the accuracy of these PM sensors is limited, they are sufficiently precise relative to one another and to research grade instruments that they can be useful is assessing the spatial and temporal variations across an area and provide concentration estimates based on higher-quality central site monitoring data.