1,720,988 research outputs found
A long-term source apportionment of PM2.5 in New York State during 2005–2016
Get PDFThe development and implementation of effective policies for controlling PM2.5 mass concentrations and protecting human health depend upon the identification and apportionment of its sources. In this study, the PM2.5 sources affecting 6 urban and 2 rural sites across New York State during the period 2005–2016 were determined. The extracted profiles were compared to identify state-wide common profiles. The source contributions provide detailed, long-term quantification of the emission sources across the state during the investigated period (2005–2016). Seven factors were common to all sites: secondary sulfate, secondary nitrate, spark-ignition emissions, diesel emissions, road dust, biomass burning, and pyrolyzed organic (OP) rich. The largest contributors were secondary sulfate, secondary nitrate, spark-ignition (gasoline), diesel, and OP-rich. Secondary sulfate concentrations ranged from 2.3 μg m−3 at Whiteface to 3.2 μg m−3 at Buffalo and the Bronx. The highest secondary sulfate fractional contributions were found at the rural sites (∼46% of PM2.5 mass) also showed the highest OP-rich contributions (∼19%). Secondary nitrate showed the highest concentrations at the urban sites representing ∼17% of PM2.5 mass (1.6 ± 0.3 μg m−3 on average). Urban sites also showed the highest average spark-ignition concentrations (1.7 ± 0.2 μg m−3, ∼18%) and diesel emissions (1.0 ± 0.2 μg m−3, ∼10%). During this period, secondary sulfate concentrations declined likely related to the implementation of mitigation strategies for controlling SO2 emissions and the changing economics of electricity generation. Similarly, diesel and secondary nitrate showed decreases in concentrations likely associated with the introduction of emissions controls and improved quality fuels for heavy-duty diesel on-road trucks and buses. Spark-ignition concentrations showed an increase across the state during 2014–2016 associated with the increase of registered vehicles in New York State
Differential Probability Functions for Investigating Long-Term Changes in Local and Regional Air Pollution Sources
Get PDFConditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO2) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. The differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated
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
No full textTen 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
Regional Nucleation Events and Sources of Submicron Particles in Rochester (NY)
No full textExtensive measurements of particle number concentration (PNC) and particle size distribution (PNSD) have been performed in Rochester (NY) since 2001. These long-term data allow assessment of past and current mitigation strategies for air pollution in the Northeastern US. This study investigates the three most recent years of data (2014/16).
Results show an average of 4.3·103 particles/cm3, of which 1.4·103, 2.3·103, and 0.7·103 particles cm3 were classified as nucleation (14-30 nm), Aitken nuclei (30-100 nm) and accumulation (100-470 nm) ranges, respectively. Annually, total PNC show two maxima, one on February and one during summer (May to September), while the daily pattern of PNC show two peaks related to the traffic rush hours (one in the early morning, one in the late afternoon-evening). Nucleation sized particles also show an evident increase during daytime, which is broadly comparable with the nucleation events driven by photochemical transformations.
Positive matrix factorization (PMF) was applied to identify and quantify the major airborne particle sources. Data were separately analyzed for summer, winter, and transition periods. For each period, 5 to 7 factors were apportioned and identified (nucleation, traffic, domestic and residential heating, secondary aerosol and ozone-rich aerosol). The application of PMF post-processing tools was useful to: (i) study the patterns of sources; (ii) depict the role of atmospheric photochemical processes; (iii) examine the locations of potential local sources by mean of conditional bivariate probability function analysis and (iv) investigate the role of regional transport of air masses to the concentrations of resolved sources. Finally, the contour plots of SMPS data were examined: an algorithm was applied to identify potential nucleation events and distinguish them between traffic nucleation events and regional photochemical nucleation events
Changes in ambient air pollutants in New York State from 2005 to 2019: Effects of policy implementations and economic and technological changes
Get PDFOver the past 20 years, a number of regulatory efforts have been applied to improve air quality in the United States and specifically in New York State. These measures generally focused on mobile emissions through emissions controls and improved fuel quality, and controls on electricity generation to reduce emissions from older, uncontrolled electricity generation units (EGUs). In addition, economic drivers such as the major recession in 2007–2009 and the change in the relative costs of natural gas and coal also drove changes in the mixture of EGU technologies. To assess the effects of these changes and to define the baseline for future changes as the economy further decarbonizes through renewable electricity generation and electric vehicles, the concentrations of all pollutants measured at all regulatory monitoring sites in New York State were assessed for their trends. Trends were examined using seasonal-trend decomposition with local regression smoothing (STL), Mann-Kendall trend analysis with the Theil-Sen nonparametric slope estimation, and piecewise regression analysis to identify breakpoints in the slopes of the time series data. The concentrations of primary gaseous pollutants, CO, NO2, and SO2 have decreased substantially in step with the declining emissions. PM2.5 has substantially declined largely due to the reductions in particulate sulfate. However, in recent years, the rate of decline has diminished due to relatively constant or increasing particulate nitrate and secondary organic aerosol. O3 has also generally increased at the urban sites likely as a result of reduced NOx emissions, while it declined or remained constant at the rural sites. Thus, the promulgated regulations assisted by the economic drivers have improved air quality, but additional actions will be needed to further reduce urban O3 and PM2.5
Evaluation and Field Calibration of a Low-cost Ozone Monitor at a Regulatory Urban Monitoring Station
Get PDFThe performance of a low cost ozone monitor (Aeroqual Series 500 portable gas monitors coupled with a metal oxide sensor for ozone; model OZL) was assessed under field conditions. Ten ozone monitors were initially calibrated in clean-air laboratory conditions and tested at controlled ozone concentrations of 5 to 100 ppb. Results showed good linearity and fast response with respect to a conventional research-grade ozone monitor. One monitor was then co-located at a regulatory air quality monitoring station that uses a U.S. federal equivalent method (FEM) ozone analyzer. Raw data from the Aeroqual monitor collected over 4 months (June–October) at a 10-minute time-resolution, showed good agreement (r2 = 0.83) with the FEM values but with an overestimation of ~12%. Data were averaged to different time resolutions; 1 h time averaged concentrations showed the best fit with the FEM results (r2 = 0.87). An analysis of the ratio of FEM/monitor concentrations against chemical and meteorological variables suggested the potential of interferences due to temperature, relative humidity, nitrogen oxides, and volatile organic compounds. Three correction models using temperature, humidity, and nitrogen dioxide (NO2) were then tested to better relate the monitor concentrations to the FEM values. Temperature and humidity are two variables commonly available (or easily measurable) at sampling sites. The model (#3) that added NO2 did not provide a substantial improvement in the fit. Thus, the proposed models with only temperature and humidity can be easily adopted and adapted by any user. The corrected data explained up to 91% of the variance and showed statistically significant improvement of the goodness of fits as well as decreased influence of the interfering variables on the diurnal and weekly patterns. The correction models were also able to lower the effect of seasonal temperature changes, allowing the use of the monitors over long-term sampling campaigns. This study demonstrated that the Aeroqual ozone monitors can return “FEM-like” concentrations after appropriate corrections. Therefore, data provided by a network of monitors could determine the intra-urban spatial variations in ozone concentrations. These results suggest that these monitors could provide more accurate human exposure assessments and thereby reduce exposure misclassification and its resulting bias in epidemiological studies
Changes in PM2.5 Sources across New York State during 2005-2015
No full textThe highest mass concentrations of airborne fine particulate
matter (PM2.5) and PM-bound sulfate of North America are
recorded over the Eastern US, which also exhibits high
concentrations of ammonium and elemental carbon. However,
the emission scenario has significantly changed during the last
decades (e.g., US standards for vehicle emissions, the
implementation of the highway diesel fuel sulfur program, use
of heating oil containing less than 15 ppm sulfur).
This study aims to detect the trends of the most impacting
PM2.5 sources across the state of New York in the last decade
(2005-2015). PM2.5 chemical speciation data were retrieved
from the US-EPA Chemical Speciation Network (CSN,
https://aqs.epa.gov/api). PM2.5 samples were collected in 8 sites
scattered over the State and characterized by different emission
scenarios: 6 urban (Albany, Buffalo, Rochester, New York -
Bronx, New York-Division St, New York - Queens) and 2 rural
(Pinnacle and Whiteface) background sites.
Samples were analyzed for PM2.5 mass, water-soluble inorganic
ions (NH4
+, K+, Na+, NO3
-, SO4
2-), elemental (EC) and organic
(OC) carbon and elements with atomic number ≥1. The final
dataset was split in pre-IMPROVE and post-IMPROVE OC/EC
analysis.
EPA PMF 5.0 was applied to identify and apportion the most
impacting sources on PM2.5 over the sites. Subsequently, the
Theil-Sen method coupled with the non-parametric Mann-
Kendall approach has been used to analyze the inter-annual
trends of extracted PM sources.
For example, 9 PM2.5 sources were extracted for Queens all
over the period: ammonium sulfate (accounting for 34% of
PM2.5mass), ammonium nitrate (17%), gasoline (15%), diesel
(10%), biomass burning (6%), aged sea salt (5%), road dust
(4%), residual oil (3%) and fresh sea salt (2%). Among the
main PM2.5 contributors, a significant decrease was observed
for ammonium sulfate (-7% y-1) and ammonium nitrate (-6% y-
1) whereas the emissions associated with gasoline vehicles
increased (+6% y-1)
PM2.5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences
Get PDFOver the past decades, mitigation strategies have been adopted both by federal and state agencies in the United States (US) to improve air quality. Between 2007 and 2009, the US faced a financial/economic crisis that lowered activity and reduced emissions. At the same time, changes in the prices of coal and natural gas drove a shift in fuels used for electricity generation. Seasonal patterns, diel cycles, spatial gradients, and trends in PM2.5 and gaseous pollutants concentrations (NOx, SO2, CO and O3) monitored in New York State (NYS) from 2005 to 2016 were examined. Relationships between ambient concentrations, changes in NYS emissions retrieved from the US EPA trends inventory, and economic indicators were studied. PM2.5 and primary gaseous pollutants concentrations decreased across NYS. By 2016, PM2.5 and SO2 attained relatively homogeneous concentrations across the state. PM2.5 concentrations decreased significantly at all sites. Similarly, SO2 concentrations declined at all sites within this period, with the highest slopes observed at the urban sites. Reductions in NOx emissions likely contributed to summertime average ozone reductions. NOx and VOCs controls reduced O3 peak concentrations at rural and suburban sites as seen in significant relationships between the annual O3 4th-highest daily maximum 8-h concentrations and estimated NOx emissions at rural and suburban sites (r2 ∼ 0.7). Spring maxima were not reduced with most sites showing insignificant slopes or significant positive slopes (e.g., +2.6% y−1 and +2% y−1, at CCNY and PFI, respectively). Increases in autumn and winter ozone concentrations were found (e,g., 6.6 ± 0.4% y−1 on average in New York City). Significant relationships were observed between PM2.5, primary pollutants, and economic indicators. Overall, a decrease in electricity generation with coal, and the simultaneous increase in natural gas consumption for power generation, led to a decrease in PM2.5 and gaseous pollutants concentrations
Long-term trends (2005–2016) of source apportioned PM2.5 across New York State
Get PDFThe United States has experienced substantial air pollutant emissions reductions in the last two decades. Among others, emissions produced by electricity generation plants and industries were significantly lowered. Ultralow (<15 ppm) sulfur fuels were introduced for road vehicles, nonroad, rail, and maritime transport. New heavy-duty diesel trucks have been equipped with particle traps and NOx controls. Residual oil (No. 6) for space heating and for any other purpose was replaced with cleaner No. 2 and No. 4 oils. Chemical speciation of PM2.5 has been measured since 2005 at eight sites across the New York State. A prior study has identified and apportioned the major sources of PM2.5 across the State using receptor modelling (positive matrix factorization). This present study aims to investigate the long-term trends of those source-apportioned PM2.5 mass contributions from 2005 to 2016 at the eight sites: two rural sites (Pinnacle and Whiteface), three medium sized cities (Buffalo, Albany, Rochester), and three sites in the New York City metropolitan area (Bronx, Manhattan and Queens). Negative trends from 2005 to 2016 were detected across the state for secondary sulfate (from −0.19 μg/m3/y in Rochester to −0.36 μg/m3/y at BRO and QUE) and secondary nitrate (from −0.02 μg/m3/y at the rural sites to approximately −0.2 μg/m3/y at BRO and MAN). Spark-ignition vehicles were the only source type experiencing upward annual trends at all urban sites with slopes ranging from 0.02 μg/m3/y (ROC, not statistically significant) to ∼0.2 μg/m3/y (Albany, Bronx, Manhattan). Other sources exhibited different trends among the sites. The relationships of source contributions with emissions inventories were explored with regression analysis. A new trajectory model, differential concentration-weighted trajectories (DCWT), was used to examine spatial changes in sources of secondary aerosol affecting the rural sites
Hourly land-use regression models based on low-cost PM monitor data
Get PDFLand-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
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