Subraham Singh1, Glen Johnson1, David W. DuBois2, Ilias G. Kavouras  This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Department of Environmental, Occupational and Geospatial Health Sciences, City University of New York Graduate School of Public Health and Health Policy, New York, NY, USA
2 Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA

Received: March 9, 2022
Revised: May 19, 2022
Accepted: June 7, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Singh, S., Johnson, G., DuBois, D.W., Kavouras, I.G. (2022). Assessment of the Contribution of Local and Regional Biomass Burning on PM2.5 in New York/New Jersey Metropolitan Area. Aerosol Air Qual. Res. 22, 220121.


  • PM2.5 mass concentrations have been declining due to decreasing secondary sulfate.
  • Biomass burning was the predominant PM2.5 source in metropolitan NY/NJ area.
  • Primary and secondary fine particles associated with traffic emissions did change over time.


The sources of fine particulate matter (PM2.5, particles with diameter < 2.5 μm) in four monitoring sites in the New York/New Jersey metropolitan statistical area from 2007 to 2017 was apportioned by positive matrix factorization (PMF) of chemical speciation data. Biomass burning, secondary inorganic (i.e., ammonium sulfate and nitrate) and primary traffic exhausts were the predominant PM2.5 sources. The declining trends of PM2.5 mass in all four sites were very well correlated with decreasing secondary sulfate levels due to SO2 emission reductions by coal-fired power plants. The contributions of secondary nitrate, primary traffic exhausts and diesel particles did not change (or slightly increased) over time except for the Queens site, where statistically significant declines were computed. Biomass burning contributions increased in the Queens and Chester sites but declined in the Division Str and Elizabeth Lab sites, although significant interannual variability was observed. Wintertime biomass burning aerosols were most likely due to combustion of contemporary biomass for industrial and domestic heating, and it was linked to the intensity (average minimum temperature) and duration (number of freezing days) of cold weather. The annual summertime biomass burning contributions were correlated with the number and area burnt by lightning-ignited wildfires. These results indicate that PM2.5 sources in urban environments is changing from anthropogenic secondary sulfate and nitrate to carbonaceous aerosol from local anthropogenic and regional climate-driven biomass burning. This trend may counterbalance emissions controls on anthropogenic activities and modify the biological and toxicological responses and resultant health effects.

Keywords: Fine aerosol, Sources, Wildfires, Woodburning, Traffic


Atmospheric particulate matter changes the earth’s energy budget directly through scattering or absorption of the solar and infrared radiation and indirectly through modification of the thermal diffusion parameters of cloud condensation nuclei (Shikwambana et al., 2020). Inhalation of atmospheric aerosol is linked to onset of cardiovascular and cerebrovascular diseases (Rajagopalan et al., 2018) including myocardial infarctions (Evans et al., 2017), ischemic stroke (Shah et al., 2015), and cardia arrythmia (Link et al., 2013), and heart failure (Shah et al., 2013). Due to implementation of a wide range of industrial emissions controls and fuel consumption policies, primary fine particle (PM2.5, particles with aerodynamic diameter < 2.5 µm) releases and precursors of secondary inorganic aerosol species (sulfur dioxide and nitrogen oxides) were reduced up to 75% in the U.S. (U.S. EPA, 2017a). As a result, ambient PM2.5 levels also declined, albeit at slower rate due to non-linear responses in atmospheric chemistry and biomass burning emissions (Chalbot et al., 2013; Squizzato et al., 2018a; Zhang et al., 2018; Blanchard et al., 2019, 2021).

Biomass burning includes wildland fires, agricultural fires, prescribed fires and anthropogenic bioenergy usage (wood burning, use of heating oil, and coal-fired power plant for producing electricity) and it is predominantly comprised of carbonaceous aerosol (i.e., organic carbon (OC) and elemental carbon (EC)) (Lee and Chan, 2015; Masiol et al., 2017a; Squizzato et al., 2018a). The seasonal pattern is frequently an indicator of biomass burning sources with domestic heating including wood burning in the cold months and fires in the warm period (Zhang et al., 2014). There has been a rapid increase in the frequency and intensity of large wildfires that has been linked to the longer and drier summer seasons (McClure and Jaffe, 2018). This increase was associated with higher temperatures during the warmer months, earlier snowmelt, and moisture deficits (Miller and Safford, 2012). Wildfires have been observed to steadily rise from 1984 to 2011 at a rate of 0.6–1.0 annually in the Northern Rockies and Pacific Northwest regions of the U.S. (Dennison et al., 2014). Climate change accounted for 50–60% of the larger wildfires from 1970 to 2016 in Northern Rockies (Westerling, 2016) and has doubled the cumulative wildfire areas burned from 1984 to 2015 in the western U.S. (Abatzoglou and Williams, 2016; Harvey, 2016). Emissions from wildfires have been consistently shown to contribute to ambient PM2.5 levels in downwind urban metropolitan areas far away from the fires (Lall and Thurston, 2006; Jaffe et al., 2008; Chalbot et al., 2013; Blanchard et al., 2019; Masiol et al., 2019).

For the New York/New Jersey metropolitan statistical area (MSA), the most populous in the U.S., the 2020 PM2.5 weighted annual average and 24-hr concentrations were 8.5 µg m–3 and 21 µg m–3, both being below the federal national ambient air quality standards (NAAQS). The 2020 8-hr max ozone (O3) concentration was 73 ppbv (above the federal NAAQS). Secondary sulfate and nitrate, motor vehicle emissions, road dust, sea salt, and oil combustion were previously identified as the predominant PM2.5 sources (Ito et al., 2004; Li et al., 2004). More recently, biomass burning has also been recognized in the region (Blanchard et al., 2019; Masiol et al., 2017a, 2017b, 2019). The latter was associated with increasing OC levels from upwind areas (Blanchard et al., 2019, 2021; Chen et al., 2022).

The objectives for this study were: (i) to identify and quanitify the contributions of PM2.5 sources across the NY/NJ MSA; (ii) to estimate the annual trends of PM2.5 sources and (iii) to assess the role of anthropogenic and biomass burning emissions. The PM2.5 chemical speciation data in four sites during the 2007–2017 period were analyzed using factor analysis to apportion PM2.5 sources. Understanding the temporal trends of PM2.5 sources is essential to evaluate the efficacy of air pollution controls on industrial and transportation sources over the pase decades. Moreover, the possible contribution of regional and continental wildfires on PM2.5 mass and chemical content was assessed.


2.1 Sampling Sites

The concentrations of PM2.5 mass and chemical species were retrieved from the U.S. Environmental Protection Agency’s Air Data system (U.S. EPA, 2017b) for the NCore site in Queens, New York (Site #1, EPA AIRS ID: 36-081-0124) and three PM2.5 chemical speciation network (CSN) sites, Lower Manhattan (Site #2, Division Street, EPA AIR ID: 36-061-0134), Elizabeth New Jersey (NJ) (Site #3, Elizabeth Lab, EPA AIR ID 34-039-0004) and Chester NJ (Site 4, EPA AIR ID: 34-027-3001) for the 2007-2017 period. Fig. 1 shows the locations of the four sites within the NY/NJ MSA. Site #1 is at Queens College, near LaGuardia Airport in New York City. Site #2 is on the roof of Public School 124 in Lower Manhattan. Site #3 is at the intersection of interstate highways I-95 and I-278, near many industrial facilities including oil refineries. Site #4 is on the Department of Public Works building at Chester town in Morris County, NJ. The sites in Queens, Division Street and Elizabeth Lab are in heavily populated areas (more than 3,000,000 residents within 8 km). Chester, a smaller town, is in western NJ, upwind of the New York City metropolitan area.

Fig. 1. The PM2.5 chemical speciation sites, population, and major road network in NY/NJ MSA.Fig. 1. The PM2.5 chemical speciation sites, population, and major road network in NY/NJ MSA.

Daily measurements of PM2.5 mass, organic carbon (OC) and elemental carbon (EC), ions (sulfate (SO42–), nitrate (NO3), ammonium (NH4+), potassium (K+) and sodium (Na+) were measured in 1 every 3 days frequency in Sites #1, 2, 3 and 1 every 6 days in Site #4. PM2.5 particles collected on polytetrafluoroethylene membrane filters (PTFE) using the MetOne SASS/SuperSASS sampler were analysed gravimetrically for PM2.5 mass and by X-ray fluorescence spectrophotometer (PANalytical Epsilon 5 analyzer). Elemental and organic carbon were measured by the thermal optical reflectance (TOR) (Sunset analyzer) for PM2.5 collected on quartz fiber filters using the URG 3000N sampler. Water-soluble PM2.5 ions were measured by ion chromatography (Dionex ICS-2000, ICS-3000 and Aquion systems) of nylon filters using MetOne SASS/SuperSASS sampler. The U.S. EPA laboratory standard operating protocols and QA/QC protocols are described elsewhere (U.S. EPA, 2019).

2.2 Aerosol Types and Source Apportionment

The major aerosol species (inorganic secondary SO42–, NO3 and NH4+), organic mass (OM), elemental carbon (EC) and soil dust concentrations were analyzed using the Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5 mass reconstruction scheme (Malm et al., 2004) according to Eqs. (1)–(4).


where [EC], [OC], [NO3], [NH4+], [SO42–], [Al], [Si], [Ca], [Fe] and [Ti] were the elemental carbon, organic carbon, nitrate, ammonium, sulfate, aluminum, silica, calcium, iron and titanium concentrations (in µg m–3), respectively. The OC/OM factor of 1.6 was used for urban PM2.5 aerosol (Turpin and Lim, 2001). Soil dust was estimated as the sum of the crystal elements as oxides. Discrepancies between measured and reconstructed particle mass may be associated with organic aerosol with a higher OC/OM factor, that is typical of oxygenated and polyfunctional organic compounds in biomass burning aerosol (Kavouras et al., 2012). Sea salt is also not considered in the IMPROVE PM2.5 mass reconstruction scheme, which may be important in remote marine environments.

The United States Environmental Protection Agency’s Positive Matrix Factorization (PMF) model (Version 5.0) was employed (Norris et al., 2014; Hopke, 2015, 2016). Chemical species with more than 50% of measurements above the limit of detection were included. Missing concentrations and uncertainties were substituted by the geometric mean of the measured concentrations and, four times the geometric mean of measured uncertainties, respectively. The concentrations of chemical species were analyzed by a least-squares method imposing a non-negative restriction on factor (i.e., source) contributions (G(nxp)) and profiles (F(pxm)), during minimization of the objective function (Paatero, 1997). The retained sources were rotated using the Fpeak variable to reduce ambiguity of the unrotated solution. The best possible number of sources and the rotation was evaluated by a set of statistical tools (Paatero et al., 2005), and by comparison of previously published source profiles. There were up to 990 samples for Sites #1–3 and 660 samples for Site #4. In total, 25 chemical species were used. The S/N ratio varied from 2.27 to 8.49. We ran the model using the robust method for factors varying from 3 to 20 with a random seed and 20 runs per configuration during screening and 100 runs for the final solution. The Qrobust/Qexpected was 1.19. The correlation coefficient varied from 0.25 (for soluble Na+) to 0.95. An eight-factor model with a rotation with Fpeak = 1.0 was selected. Base and Fpeak bootstrapping included 200 runs using a minimum R of 0.75 and block size of 6 with random seeding. More than 80% of base and 100% of Fpeak bootstrapped factors were mapped into the original factors, with no clear pattern for the unmapped factors.

2.3 Statistical Analysis

The non-parametric Mann-Whitney U-test (for two groups) and Kruskal-Wallis (for more than 2 groups) at α = 0.05 was used to test the significant of the difference. The annual trend was computed using the monthly PM2.5 source contributions by applying the non-parametric sequential Mann-Kendall test at a confidence level of 95% (Kganyago and Shikwambana, 2020; Shikwambana et al., 2020). Analyses were done using IBM SPSS (Version 27) (IBM Analytics, Armonk, NY).


3.1 PM2.5 Types and Characteristics

Table 1 shows the PM2.5 diagnostic ratios and reconstructed aerosol type concentrations at the four sites. The K/Fe enrichment factor (EF) was computed as the ratio of measured K/Fe to the crystal Soil K/Fe (0.56). The S/SO42– mass ratio (from 3.03 to 3.67) and NH4+/SO42– molar ratio (from 2.50 to 2.77) demonstrated the presence of ammonium sulfate ((NH4)2SO4) and ammonium bisulfate ((NH4)HSO4) particles (Malm et al., 2002).The OC/EC mass ratio suggested a mixture of combustion sources with a strong fossil fuel signature in urban sites (#1, 2 and 3) (from 2.34–3.32) and biomass in Chester (Site #4, 5.83 ± 0.16). The significant contribution of biomass burning was further corroborated by the increased abundance of soluble potassium (K+, a tracer of biomass burning) (from 58 to 64% of total K) and the EF(K/Fe) values (from 0.78 to 2.56).

Table 1. PM2.5 diagnostic ratios in the NY/NJ MSA during 2007–2017.

Inorganic species were the predominant PM2.5 aerosol components (from 2.76 to 4.99 µg m–3), followed by OM (from 2.71 to 4.98 µg m–3). Inorganic species and OM concentrations accounted for more than 80% of PM2.5 mass with a west-to-east spatial gradient from Chester (#4) to Division Street (#2) and slightly decline for Queens College (#1). For EC and soil dust, the highest concentrations were measured at the Elizabeth Lab site (#3), at the intersection of two busy interstate highways. The ratio of the aerosol type concentration measured in urban sites (Queens, Division Str and Elizabeth Lab) as compared to that measured in Chester varied from 1.28–1.38 for inorganic species and 1.30–1.84 for OM, indicating that regional upwind sources may account for most of inorganic species and OM measured at the urban sites. On the other hand, the EC concentration ratio (2.18–3.76) and soil dust (2.36–3.16) indicated the significant contribution of local sources.

3.2 Source Apportionment

The reconstructed PM2.5 mass concentrations accounted from 90% at Chester to 106% at Division Street, of measured PM2.5 mass (Table 2). Figs. 2 and 3 illustrate the profiles and seasonal contributions of the eight PM2.5 sources. The mean contribution of each source of PM2.5 mass by site is presented in Table 2. The first factor was assigned to biomass burning with high concentrations of OC, EC, S and SO42– (Fig. 2). The OC/EC ratio (4.92 ± 0.10) was indicative of contemporary biomass burning (Turpin and Lim, 2001). The SO42–/S ratio (2.57 ± 0.13) was characteristic of a mixture of fresh and aged aerosol (Malm et al., 2002). Biomass burning contributed from 2.9 ± 0.1 µg m–3 at Queens College to 3.5 ± 0.1 µg m–3 at Elizabeth Lab (Table 2) with slightly higher contributions in summer and winter as compared to fall and spring (Fig. 3). Biomass burning accounted for about 37% of PM2.5 in urban sites and 55% of PM2.5 mass in Chester. The seasonal trend suggested the influence of local residential wood burning and regional wildland fires (Chalbot et al., 2013).

Table 2. The mean (± standard error) contributions of sources to PM2.5 mass in the NY/NJ MSA during 2007–2017.

Fig. 2. Profiles of PM2.5 sources in the NY/NJ MSA during 2007–2017.Fig. 2Profiles of PM2.5 sources in the NY/NJ MSA during 2007–2017.

Fig. 3. Seasonal contributions of PM2.5 sources in the NY/NJ MSA during 2007–2017.Fig. 3Seasonal contributions of PM2.5 sources in the NY/NJ MSA during 2007–2017.

The second factor was assigned to secondary NO3 (in the form of NH4NO3) with high concentrations of NO3, NH4+, OC, EC, K+, K and SO42– (Fig. 2). The contribution of secondary NO3 particles varied from 0.4 ± 0.1 µg m–3 in Chester to 0.7 ± 0.1 µg m–3 in Queens (from 6% to 8% of PM2.5 mass) with the highest contributions during winter and the lowest during summer (Fig. 3). This was consistent with the favorable conditions for gas-to-particles conversion of HNO3 in low ambient temperatures. The third factor was attributed to diesel combustions because of the high Ni, V, EC, NO3, NH4+, SO42– and to a lesser extent to Fe and Mn concentrations (Chalbot et al., 2013). The presence of NO3 and SO42– indicated the contribution from both industrial activities and transportation diesel engines. It accounted for less than 0.1 µg m–3 of PM2.5 mass in Chester, up to 0.7 ± 0.1 µg m–3 in Queens (Table 2) (from 5% to 8% of PM2.5 mass in Queens and Division Str. and less than 2% in Elizabeth Lab and Chester) with higher concentrations in the winter (Fig. 3).

The high concentrations of crystal Al, Si, Ca, Fe and Ti on the fourth factor indicated the presence of soil particles. The components OC, EC, SO42–, S and Mg hinted at the mechanical resuspension of contaminated road dust (Fig. 2). Mineral and road dust accounted from 0.1 ± 0.1 µg m–3 in Chester to 0.4 ± 0.1 µg m–3 (from 2% to 4% of PM2.5 mass). The highest contributions were computed in spring and summer due to accumulation during the winter of dust and debris deposited in curbs and road shoulders available for resuspension by traffic and patterns of regional dust transport (Fig. 3) (Chalbot et al., 2013). Sea salt particles are correlated with Na, Na+ and Cl in the sixth factor. It also exhibited high contributions from S, SO42– and, to a lesser extent, NO3 and OC, indicating the possible contribution of harbor and shipping emissions. This was further supported by the SO42–/S ratio (3.00 ± 0.36) and lack of NH4+, indicating free H2SO4. The New York port is the destination of container ships (37%), oil/chemical tankers (9%), passenger cruise ships (7%), vehicles carriers (6%) and crude oil tanker (5%). It contributed, on average, less than 0.1 µg m–3 on PM2.5 mass concentration (less and 1%) across all four sites. Slightly higher contributions were computed in spring than those measured in winter and summer (Fig. 3).

The sixth factor was attributed to secondary SO42– because of the high levels of S, SO42–, NH4+ and to a lesser extent of EC and OC. This was further supported by the SO42–/S ratio (3.02 ± 0.09). This source accounted for 1.5 ± 0.1 µg m–3 of PM2.5 in Chester to 2.4 ± 0.1 µg m–3 of PM2.5 in Elizabeth Lab (from 24% to 28%), with the highest contributions being measured in the summer (Fig. 3). Traffic exhausts were identified due to OC, EC, S, SO42–, NO3, Zn and Fe high concentrations in the seventh factor. The low OC/EC ratio (1.84 ± 0.07) was comparable to those computed for urban aerosol and traffic exhausts (Turpin and Lim, 2001). Traffic added 1.5 ± 0.1 µg m–3 of PM2.5 in Chester up to 2.2 ± 0.1 µg m–3 of PM2.5 in Elizabeth Lab (Table 2) (17–23%) with slightly higher contribution in the fall at Elizabeth Lab and Division Street and no seasonal variation at Queens and Chester (Fig. 3). Lastly, ferrous, and chrome-related industrial emissions were identified because of the concentrations of heavy metals (Cr, Fe, Ni, Cu). This source contributed minimally (less than 0.1 µg m–3 of PM2.5; less than 1%) throughout the year (Table 2).

3.3 Annual Trends

The annual trends of PM2.5 mass and source contributions are presented in Table 3. The p-value of the trend analysis is shown in parentheses if p > 0.001). PM2.5 mass declined by 0.23 ± 0.02 µg m–3 yr–1 in Chester to 0.54 ± 0.05 µg m–3 yr–1 in Division Str (p < 0.001) This was mostly due to the significant decrease of secondary SO42– (from –0.19 ± 0.02 µg m–3 yr–1 in Chester to –0.36 ± 0.03 µg m–3 yr–1 in Division Str (p < 0.001). The declining trends among all four sites were indicative of the regional SO42– origins from power plants (Emami et al., 2018). The annual SO42– concentration in the NY/NJ MSA dropped by 77.5%, from 3.8 µg m–3 in 2007 to 0.9 µg m–3 in 2017. This was comparable to national SO2 reductions (78.2%, from 11.7 × 106 tons in 2007 to 1.9 × 106 tons in 2017) (U.S. EPA, 2017a).

For locally important sources such as secondary nitrate, traffic exhausts and diesel emissions, site-specific annual trends were observed. More specifically, secondary NO3 and primary traffic-related gasoline exhausts declined in Queens (–0.19 ± 0.02 µg m–3 yr–1 for secondary NO3 and –0.04 ± 0.01 µg m–3 yr–1 for traffic exhausts, p < 0.001) but slightly increased in the remaining sites, albeit without statistically insignificance (Table 3) The NO3 annual concentrations declined, from 1.7 µg m–3 in 2007 to 1.0 µg m–3 in 2017 was consistent with the national NOx emission reduction (45.6%) (U.S. EPA, 2017a). The good agreement between reductions of secondary SO42– and NO3 and their precursors emissions (SO2 and NOx), despite that NH3 emissions remained relatively unchanged nationally, further confirmed strong regional contributions and the negligible influence of local NH3 emissions. For diesel exhausts, contributions decreased in the three urban sites, from –0.02 ± 0.01 µg m–3 yr–1 in Elizabeth Lab to –0.06 ± 0.01 µg m–3 yr–1 in Queens and Division Str. The annual trend of mineral and road dust was comparable to that of traffic exhausts, with an increasing trend in Division Str (0.01 ± 0.01 µg m–3 yr–1) and Elizabeth Lab (0.04 ± 0.01 µg m–3 yr–1) and minimal changes in Queens and Chester. No significant trends were computed for contaminated sea salt and industrial emissions.

Table 3. The mean (± standard error) annual trend of PM2.5 sources in the NY/NJ MSA during 2007–2017.

Biomass burning contributions declined substantially in Elizabeth Lab (–0.12 ± 0.01 µg m–3 yr–1, p < 0.001) and Division Str (–0.04 ± 0.01 µg m–3 yr–1, p = 0.22) but increased in Chester (slightly, 0.01 ± 0.01 µg m–3 yr–1, p = 0.1) and Queens (0.07 ± 0.01 µg m–3 yr–1, p < 0.001). The inconsistent annual trends may be influenced by climatology and local domestic emissions (Rattigan et al., 2016; Squizzato et al., 2018b; Pitiranggon et al., 2021). Wood burning for heating and recreational reasons is allowed in both New York and New Jersey; however due to the type of residential units, its prevalence may be higher in communities with single house units (e.g., Chester and Queens) as compared to building apartments (e.g., Division Str). The site in Elizabeth Lab is in an industrial area.

3.4 Biomass Burning Sources

Fig. 4 illustrates the annual trends of biomass burning in summer (May–September) and winter (November to March), the number and area burnt (in acres) by lightning ignited wildland fires in the U.S. (data obtained from the National Interagency Fire Center) and the monthly minimum temperature and number of days with temperature less than 0°C in New York City (New York City Central Park; NOAA NCDC site: USW00094728) during the 2007–2017 period. Since 2010, there appears to be a temporal correlation of the number of wildfires naturally induced by lightning and, to a lesser extent, the area burnt and summertime biomass burning contributions. Wildfires burned more than 15,000 km2 per year in the U.S. in 2007–2009, as compared to 17,000 km2 during the 2011–2013 period and over 20,000 km2 during the 2015–2017 period. We have previously shown that changes in the El-Nino southern oscillation were correlated with wildfires in Eastern United States and episodes of smoke-related high ozone pollution (Singh and Kavouras, 2022). Higher wintertime biomass burning contributions prior to 2010 and in 2014 were associated with colder winters with an average minimum temperature of less than 9°C and prolonged periods of extreme cold more than 60 days. On the other hand, local biomass burning contributions declined when the average minimum temperature was above 0°C and the number of cold days declined.

Fig. 4. Annual trend of biomass burning contributions in summer (May–September) and winter (November–March), number and area burnt by wildfires and, minimum winter temperatures and number of days with temperature less than 0°C.Fig. 4. Annual trend of biomass burning contributions in summer (May–September) and winter (November–March), number and area burnt by wildfires and, minimum winter temperatures and number of days with temperature less than 0°C.


The sources of fine particles in the NY/NJ MSA for the 2007–2017 period were biomass burning (37–55%), secondary sulfate (24–28%), primary traffic emissions (17–23%), secondary nitrate (6–8%), diesel emissions (1–8%), road dust (2–4%), sea salt (< 1%), and industrial emissions (< 1%). The seasonality of secondary NO3, secondary SO42– and dust was consistent with those previously observed in urban environments and the effect of local meteorology and emissions. PM2.5 mass concentrations declined by 0.23–0.54 µg m–3 yr–1. Secondary NO3 and secondary SO42– declines were consistent with the reductions on NOx and SO2 emission from mobile and point sources, respectively. The seasonal variability of biomass burning to PM2.5 mass was indicative of local wood burning for domestic heating and recreational activities and regional smoke from wildfires contributed to PM2.5 in winter and summer, respectively. Wintertime biomass burning appeared to be related to the number of cold days and average minimum temperature in the region. The number and area burnt by wildfires was also associated with the interannual variability of biomass burning contribution in the summer. The findings of this study show that changes in PM2.5 mass concentrations in the NY/NJ MSA are responding to reductions of secondary sulfate and nitrate precursors from anthropogenic sources. As a result, biomass burning is the predominant PM2.5 source. Its contributions appear to be related to local and regional climatology affecting the frequency and intensity of cold weather in the winter and wildfires in the summer.


The data analysis by Subraham Singh was supported in part by the Dean’s Dissertation Award.


The authors declare no potential conflicts of interest.


  1. Abatzoglou, J.T., Williams, A.P. (2016). Impact of anthropogenic climate change on wildfire across western US forests. PNAS 113, 11770–11775.

  2. Blanchard, C.L., Shaw, S.L., Edgerton, E.S., Schwab, J.J. (2019). Emission influences on air pollutant concentrations in New York state: II. PM2.5 organic and elemental carbon constituents. Atmos. Environ.: X 3, 100039.

  3. Blanchard, C.L., Shaw, S.L., Edgerton, E.S., Schwab, J.J. (2021). Ambient PM2.5 organic and elemental carbon in New York City: Changing source contributions during a decade of large emission reductions. J. Air Waste Manage. Assoc. 71, 995–1012.​10962247.2021.1914773

  4. Chalbot, M.C., McElroy, B., Kavouras, I. (2013). Sources, trends and regional impacts of fine particulate matter in southern Mississippi Valley: significance of emissions from sources in the Gulf of Mexico coast. Atmos. Chem. Phys. 13, 3721–3732.

  5. Chen, Y., Rich, D.Q., Hopke, P.K. (2022). Long-term PM2.5 source analyses in New York City from the perspective of dispersion normalized PMF. Atmos. Environ. 272, 118949.​10.1016/j.atmosenv.2022.118949

  6. Dennison, P.E., Brewer, S.C., Arnold, J.D., Moritz, M.A. (2014). Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933.​2014GL059576

  7. Emami, F., Masiol, M., Hopke, P.K. (2018). Air pollution at Rochester, NY: Long-term trends and multivariate analysis of upwind SO2 source impacts. Sci. Total Environ. 612, 1506–1515.

  8. Evans, K.A., Hopke, P.K., Utell, M.J., Kane, C., Thurston, S.W., Ling, F.S., Chalupa, D., Rich, D.Q. (2017). Triggering of ST-elevation myocardial infarction by ambient wood smoke and other particulate and gaseous pollutants. J. Exposure Sci. Environ. Epidemiol. 27, 198–206.

  9. Harvey, B.J. (2016). Human-caused climate change is now a key driver of forest fire activity in the western United States. Proc. Natl. Acad. Sci. U.S.A. 113, 11649–11650.​10.1073/pnas.1612926113

  10. Hopke, P.K. (2015). Applying Multivariate Curve Resolution to Source Apportionment of the Atmospheric Aerosol, in: 40 Years of Chemometrics – From Bruce Kowalski to the Future, American Chemical Society, pp. 129–157.

  11. Hopke, P.K. (2016). Review of receptor modeling methods for source apportionment. J. Air Waste Manage. Assoc. 66, 237–259.

  12. Ito, K., Xue, N., Thurston, G. (2004). Spatial variation of PM2.5 chemical species and source-apportioned mass concentrations in New York City. Atmos. Environ. 38, 5269–5282.

  13. Jaffe, D., Hafner, W., Chand, D., Westerling, A., Spracklen, D. (2008). Interannual variations in PM2.5 due to wildfires in the western United States. Environ. Sci. Technol. 42, 2812–2818.

  14. Kavouras, I.G., Nikolich, G., Etyemezian, V., DuBois, D.W., King, J., Shafer, D. (2012). In situ observations of soil minerals and organic matter in the early phases of prescribed fires. J. Geophys. Res. 117, D12310.

  15. Kganyago, M., Shikwambana, L. (2020). Assessment of the characteristics of recent major wildfires in the USA, Australia and Brazil in 2018–2019 using multi-source satellite products. Remote Sens. 12, 1803.

  16. Lall, R., Thurston, G.D. (2006). Identifying and quantifying transported vs. local sources of New York City PM2.5 fine particulate matter air pollution. Atmos. Environ. 40, 333–346.

  17. Lee, W.W., Chan, W.T. (2015). Calibration of single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS). J. Anal. At. Spectrom. 30, 1245–1254.​C4JA00408F

  18. Li, Z., Hopke, P.K., Husain, L., Qureshi, S., Dutkiewicz, V.A., Schwab, J.J., Drewnick, F., Demerjian, K.L. (2004). Sources of fine particle composition in New York City. Atmos. Environ. 38, 6521–6529.

  19. Link, M.S., Luttmann-Gibson, H., Schwartz, J., Mittleman, M.A., Wessler, B., Gold, D.R., Dockery, D.W., Laden, F. (2013). Acute exposure to air pollution triggers atrial fibrillation. J. Am. Coll. Cardiol. 62, 816–825.

  20. Malm, W.C., Schichtel, B.A., Ames, R.B., Gebhart, K.A. (2002). A 10-year spatial and temporal trend of sulfate across the United States. J. Geophys. Res. 107, 4627.​10.1029/2002JD002107

  21. Malm, W.C., Schichtel, B.A., Pitchford, M.L., Ashbaugh, L.L., Eldred, R.A. (2004). Spatial and monthly trends in speciated fine particle concentration in the United States. J. Geophys. Res. 109, D03306

  22. Masiol, M., Hopke, P., Felton, H., Frank, B., Rattigan, O., Wurth, M., LaDuke, G. (2017a). Analysis of major air pollutants and submicron particles in New York City and Long Island. Atmos. Environ. 148, 203–214.

  23. Masiol, M., Hopke, P., Felton, H., Frank, B., Rattigan, O., Wurth, M., LaDuke, G. (2017b). Source apportionment of PM2.5 chemically speciated mass and particle number concentrations in New York City. Atmos. Environ. 148, 215–229.

  24. Masiol, M., Squizzato, S., Rich, D.Q., Hopke, P.K. (2019). Long-term trends (2005–2016) of source apportioned PM2.5 across New York State. Atmos. Environ. 201, 110–120.​10.1016/j.atmosenv.2018.12.038

  25. McClure, C.D., Jaffe, D.A. (2018). US particulate matter air quality improves except in wildfire-prone areas. PNAS, 115: 7901–7906.

  26. Miller, J.D., Safford, H. (2012). Trends in wildfire severity: 1984 to 2010 in the Sierra Nevada, Modoc Plateau, and southern Cascades, California, USA. Fire Ecol. 8, 41–57.​10.4996/fireecology.0803041

  27. Norris, G., Duvall, R., Brown, S., Bai, S. (2014). EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. EPA/600/R-14/108. U.S. Environmental Protection Agency, Washington, DC.

  28. Paatero, P. (1997). Least squares formulation of robust non-negative factor analysis. Chemom. Intell. Lab. Syst., 37, 23–35.

  29. Paatero, P., Hopke, P.K., Begum, B.A., Biswas, S.K. (2005). A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmos. Environ. 39, 193–201.

  30. Pitiranggon, M., Johnson, S., Haney, J., Eisl, H., Ito, K. (2021). Long-term trends in local and transported PM2.5 pollution in New York City. Atmos. Environ. 248, 118238.​10.1016/j.atmosenv.2021.118238

  31. Rajagopalan, S., Al-Kindi, S.G., Brook, R.D. (2018). Air pollution and cardiovascular disease. J. Am. Coll. Cardiol. 72, 2054–2070.

  32. Rattigan, O.V., Civerolo, K.L., Felton, H.D., Schwab, J.J., Demerjian, K.L. (2016). Long term trends in New York: PM2.5 mass and particle components. Aerosol Air Qual. Res. 16, 1191–1205.

  33. Shah, A.S., Langrish, J.P., Nair, H., McAllister, D.A., Hunter, A.L., Donaldson, K., Newby, D.E., Mills, N.L. (2013). Global association of air pollution and heart failure: A systematic review and meta-analysis. Lancet 382, 1039–1048.

  34. Shah, A.S.V., Lee, K.K., McAllister, D.A., Hunter, A., Nair, H., Whiteley, W., Langrish, J.P., Newby, D.E., Mills, N.L. (2015). Short term exposure to air pollution and stroke: Systematic review and meta-analysis. BMJ 350, h1295.

  35. Shikwambana, L., Mhangara, P., Mbatha, N. (2020). Trend analysis and first time observations of sulphur dioxide and nitrogen dioxide in South Africa using TROPOMI/Sentinel-5 P data. Int. J. Appl. Earth Obs. Geoinf. 91, 102130.

  36. Singh, S., Kavouras, I.G. (2022). Trends of ground-level ozone in New York city area during 2007–2017. Atmosphere 13, 114.

  37. Squizzato, S., Masiol, M., Rich, D.Q., Hopke, P.K. (2018a). PM2.5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences. Atmos. Environ. 183, 209–224.

  38. Squizzato, S., Masiol, M., Rich, D.Q., Hopke, P.K. (2018b). A long-term source apportionment of PM2.5 in New York State during 2005–2016. Atmos. Environ. 192, 35–47.​10.1016/j.atmosenv.2018.08.044

  39. Turpin, B.J., Lim, H.J. (2001). Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol. Sci. Technol. 35, 602–610.

  40. U.S. Environmental Protection Agency (U.S. EPA) (2017a). Air Emissions Inventories. (accessed 1 September 2020).

  41. U.S. Environmental Protection Agency (U.S. EPA) (2017b). Air Data. Pre-Generated Data Files. (accessed 1 June 2020).

  42. U.S. Environmental Protection Agency (U.S. EPA) (2019). Chemical Speciation Network - Quality Assurance. (accessed 15 May 2022).

  43. Westerling, A.L. (2016). Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. London, Ser. B 371, 20150178.​10.1098/rstb.2015.0178

  44. Zhang, W., Lin, S., Hopke, P.K., Thurston, S.W., van Wijngaarden, E., Croft, D., Squizzato, S., Masiol, M., Rich, D.Q. (2018). 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. Environ. Pollut. 242, 1404–1416.​10.1016/j.envpol.2018.08.030

  45. Zhang, X., Kondragunta, S., Roy, D.P. (2014). Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011. J. Geophys. Res. 119, 1147–1162.

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