Effect of air pollution controls on black smoke and sulfur dioxide concentrations across Ireland

During the 1980s Ireland experienced severe pollution episodes, principally because of domestic coal burning. In 1990, the Irish government introduced a ban on the marketing, sale, and distribution of coal in Dublin. They extended the ban to Cork in 1995 and to ten other communities in 1998 and 2000. We previously reported declines in particulate (black smoke [BS]) and sulfur dioxide (SO2) concentrations in Dublin following the 1990 coal ban. We now explore and compare the effectiveness of these sequential bans in 1990, 1995, 1998, and 2000. Daily BS and total gaseous acidity (502) measurements were compiled between 1980 and 2004. We calculated descriptive statistics for the pre-ban (5 yr before ban) and post-ban (5 yr after ban) periods for BS and SO2 concentrations and for season-specific periods. Mean BS levels fell in all centers post-ban compared with the pre-ban period, with decreases ranging; from 4 to 35 mu g.m(-3) (-45 to -70%). These reductions were smallest in the summer and largest in the winter. These BS, reductions were sustained in all centers until the end of the study period. We observed no clear pattern in SO2 changes associated with the coal bans. The 1990, 1995, 1998, and 2000 Irish coal sale bans resulted in immediate and sustained decreases in particulate levels in centers, with the largest declines in the winter. In contrast, we did not observe consistent declines in total acidity as a measure of SO2. It may be that coal was not the major source. of SO2. Simple legislation was very effective at improving ambient air quality in Irish cities with varying populations, geography/topography, and meteorological conditions

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

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

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

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

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

Suspected association of ventricular arrhythmia with air pollution in a motorbike rider: a case report

<p>Abstract</p> <p>Introduction</p> <p>Premature ventricular complexes are to some extent a normal finding in healthy individuals and the prevalence increases with age and is more common in men. Premature ventricular complexes can occur in association with a variety of stimuli, and a lesser known cause is the association between air pollution and ventricular arrhythmias.</p> <p>Case presentation</p> <p>A previously healthy man started to ride a lightweight motorbike in heavy traffic. A few weeks later he was admitted to hospital with premature ventricular complexes in bigeminy, which decreased after a few days when he was not exposed to exhaust fumes. A few weeks later he started using the motorbike again and the same symptoms developed once more, only to subside when he stopped riding in heavy traffic.</p> <p>Conclusion</p> <p>Studies have shown an association between air pollution and premature ventricular complexes and other kinds of arrhythmias. The mechanism may be changes in cardiac autonomic function, including heart rate and heart rate variability. Air pollution should be considered when patients present with arrhythmias and no other causes are found.</p

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

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