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

There have been many changes in the air pollutant sources in the northeastern United States since 2001. To assess the effect of these changes, trend analyses of the monthly average values were performed on PM2.5 and its components including major ions, elemental carbon (EC), organic carbon (OC), and gaseous pollutant concentrations measured between 2001 (in some cases 1999) and 2015 at the NYS Department of Environmental Conservation sites in Rochester, NY. Mann-Kendall regression with Sen's slope was applied to estimate the trends and seasonality. Using piecewise regression, significant reductions in the air pollution of Rochester area were observed between 2008 and 2010 when a 260 MW coal-fired power plant was decommissioned, new heavy-duty diesel trucks had to be equipped with catalytic regenerator traps, and the economic recession that began in 2008 reduced traffic and other activities. The monthly average PM2.5 mass showed a downward trend (− 5 μg/m3; − 41%) in Rochester between 2001 and 2015. This change is largely due to reductions in particulate sulfate that showed a 65% decrease. The sulfate concentrations were compared to changes in SO2 emissions in seventeen upwind source domains, and other systematic changes by multivariate linear regression. Selectivity ratio obtained from target projection discriminated the most important source domains that are SO2 emissions from Georgia for winter, North Carolina for transition (spring and fall) and Ohio along with other influences for summer. North Carolina and Michigan were identified as the main sources for entire period. These observations suggest that any further reductions in the specified regional SO2 emissions would result in a proportional decrease in sulfate in Rochester

Changes in ambient air pollutants in New York State from 2005 to 2019: Effects of policy implementations and economic and technological changes

Over 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

Application of receptor modelling methods.

The use of atmospheric compositional data for the identification and apportionment of sources has been ongoing for more than 40 years. Beginning in the 1960s, it was recognized that data analysis techniques could be applied to data and resolve combination of constituents that represent sources. In the late 1970s, these data analysis tools came to be called Receptor Models. This paper traces the early history of receptor models through those early papers and provides a historical introduction to the paper in this special issue showing the state of the art in the field and the application of these modern tools to a variety of atmospheric data. © 2011 Turkish National Committee for Air Pollution Research and Control (TUNCAP)

PM2.5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences

Over 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

The 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

A long-term source apportionment of PM2.5 in New York State during 2005–2016

The 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

Performance Evaluation of Two 25 kW Residential Wood Pellet Boiler Heating Systems

A significant increase in the use of wood pellets for residential space heating has occurred over the past decade. The performance of two modern residential wood pellet boilers (designated PB and WPB) were evaluated including boiler thermal efficiency, thermal energy storage (TES) tank discharge efficiency, and system efficiency. A correlation applicable to both systems between the boiler thermal efficiency (ηth, in %) and the boiler output load (χ, in %) was found in the form of ηth = 52.69 ln χ – 137.7 with R2 = 0.79 (for 25 < χ < 75). This equation provides an easy, accurate estimation of the boiler thermal efficiency in field operations. The boiler thermal efficiency decreased with time and this decline was determined using a Mann-Kendall trend analysis with Sen’s slope. This decrease was primarily the result of fouling in the heat exchanger and thus, this analysis identifies the need for manual cleaning of the heat exchanger tubes to restore maximal system performance. The evaluation of the TES tank performance found that the TES tank discharge efficiency was correlated with a dimensionless function of tank inlet Reynolds number (Red) and temperature differences in the tank and inlet and outlet pipes. Overall system efficiency showed a seasonal average of 62.8%, 62.0%, and 75.8% for three heating seasons of the PB system. These results provide a comprehensive performance evaluation of these wood pellet boiler heating systems in the field over an extended period of operation

Evaluation and Field Calibration of a Low-cost Ozone Monitor at a Regulatory Urban Monitoring Station

The 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

Differential Probability Functions for Investigating Long-Term Changes in Local and Regional Air Pollution Sources

Conditional 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

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