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Particulate matter sources and long-term trends in a small New Zealand city
Atmospheric Pollution Research 6 (2015) 1105e1112
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Original article
Particulate matter sources and long-term trends in a small New Zealand city Travis Ancelet*, Perry K. Davy, William J. Trompetter GNS Science, 30 Gracefield Road, PO Box 31312, Lower Hutt, New Zealand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 March 2015 Received in revised form 14 June 2015 Accepted 15 June 2015 Available online 12 October 2015
Particulate matter samples (PM10 and PM2.5) in downtown Nelson, New Zealand were collected from 2006 to 2012. These samples were used to investigate sources of PM10 and PM2.5, and to evaluate longterm trends in PM10 and BC concentrations. Five PM10 and PM2.5 sources were identified using positive matrix factorization: biomass combustion, motor vehicles, secondary sulfate, marine aerosol and soil. Overall, biomass combustion was the dominant contributor to PM10 (48%) and PM2.5 (77%) mass. The biomass combustion factor profile featured arsenic, suggesting that locals were burning copper chrome arsenate-treated timber, an activity that appears to occur throughout New Zealand. Trend analyses on PM10 and black carbon concentrations revealed that both were decreasing year-onyear, at an average rate of 0.5 mg m3 per year and 100 ng m3 per year, respectively. This study provides important information for Nelson City Council, who are responsible for managing air quality in Nelson, to effectively manage air quality. This study also shows that relatively simple mitigation measures can instigate decreases in PM and BC concentrations. Copyright © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: Particulate matter Source apportionment Trend analysis Biomass burning Arsenic
1. Introduction It is well-established that particulate matter (PM) has adverse effects on human health and the environment (Dockery et al., 1993; € schl, 2005; Pope and Dockery, Samet et al., 2000; Nel, 2005; Po sfai and Buseck, 2010). The International Agency for 2006; Po Research on Cancer (IARC) has even declared that outdoor pollution, and specifically PM, is carcinogenic to humans (IARC, 2013). Because of these effects, PM concentrations are routinely monitored worldwide and managed according to local legislation. In New Zealand, the National Environmental Standard (NES) for PM sets a 24-h average limit for PM10 (particulate matter with aerodynamic diameters less than 10 mm) concentrations at 50 mg m3 that can only be exceeded once per year. Many urban areas in New Zealand exceed the NES numerous times each year, particularly during the winter when wood combustion for home heating is common. No standard exists for PM2.5 in New Zealand. The myriad of adverse health and environmental effects associated with PM are
* Corresponding author. Tel.: þ64 4 570 4668; fax: þ64 4 570 4657. E-mail address: [email protected] (T. Ancelet). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
related to a number of factors, including particle size, composition and surface area (Hannigan et al., 2005; Staniswalis et al., 2005). As such, size-resolved information about PM sources and their contributions to ambient PM concentrations is critical for effective air quality management. To identify the sources contributing to measured PM concentrations, multivariate receptor models are used. Positive matrix factorization (PMF) is a powerful and commonly used multivariate receptor model that is capable of resolving factors, or PM sources, without prior source knowledge. It is however, important to note that source-specific profiles (fingerprints) must be known to properly assign the PMF model outputs. PMF has a number of advantages over traditional factor analysis techniques including nonnegativity constraints and the ability to accommodate missing or below detection limit data. The results of the analysis are directly interpretable as mass contributions from each factor (Paatero and Tapper, 1994; Paatero, 1997; Song et al., 2001). Two receptor models are available to perform PMF, PMF2 (Paatero, 1997) and EPA PMF (USEPA, 2008; USEPA, 2014). EPA PMF incorporates a graphical user interface and a bilinear model that is solved by the Multilinear Engine (Paatero, 1999). Previous studies have shown that EPA PMF and PMF2 produce similar results, with only minor differences in the final solutions (Kim and Hopke, 2007; Hwang and Hopke, 2011).
http://dx.doi.org/10.1016/j.apr.2015.06.008 1309-1042/Copyright © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
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Nelson, located on the northern coast of New Zealand's South Island (latitude 41.16 , longitude 173.17 ), is a small city with a population of approximately 43 000. Nelson is known to suffer from poor air quality during the winter when wood burning for domestic heating and strong temperature inversions that limit the dispersion of pollutants are common (Trompetter et al., 2013). These conditions cause PM10 concentrations to exceed the New Zealand NES a number of times each year, which has important implications for Nelson City Council (NCC), the local body responsible for managing air pollution, because the NES cannot be exceeded more than once per year. A previous study using PMF in the Nelson suburb of Tahunanui showed that during the winter, exceedances of the NES were driven by domestic wood combustion, while exceedances during the spring and summer were the result of vehicular movements on unsealed roads (Ancelet et al., 2014a). Little is known about PM sources and their contributions in Nelson city itself; and nothing is known about the effectiveness of policy measures, including the implementation of stringent wood combustion appliance standards and educational campaigns, taken by NCC to reduce wintertime PM emissions from domestic heating. In this study we aimed to address this knowledge gap using PM2.5 and PM10 samples collected from 2008 to 2012. We also used a larger dataset of PM10 and BC concentrations to understand trends in these two pollutants. Understanding trends in PM10 and BC concentrations is critical because it highlights how effective air quality management strategies are, and whether there may be more costeffective options for reducing pollutants. Trends in BC concentrations, in particular, show how combustion emissions change over time, which is critical for many locations around the world and can have important implications for Earth's climate (Bond et al., 2013). Our approach of using BC concentrations from quartz fibre filters in combination with those from Teflon filters provides a way for researchers and air quality managers to use historical samples for measuring progress on air quality improvements. 2. Materials and methods 2.1. Sample collection and site description Particulate matter samples (PM10 and PM2.5) were collected over 24-h at an ambient air quality monitoring station located on a property off of St. Vincent Street, Nelson (Lat: 41.164150 , Long: 173.162447, elevation: 5 m). NCC were responsible for all filter changes and equipment maintenance. Fig. S1 of the Supplementary Material (SM) presents a map of New Zealand and the site location on a map of the local area. St. Vincent Street is located near (within 600 m) the Nelson central business district. The site was approximately 90 m from the nearest road and surrounded by open space or buildings no more than two stories high. Sampling began in January 2006 and continued until July 2012. From 2006 to 2008, PM10 samples at the St. Vincent Street site were collected onto quartz fibre filters using a Partisol sampler (Thermo Scientific, Waltham, MA, USA). While these samples could not be analyzed for their elemental content using IBA techniques, BC concentrations for each of the samples were determined. In total, 238 samples were collected onto quartz fibre filters from January 2006 to April 2008. In July 2008 the filter material was changed to Teflon so that the samples could be analyzed using IBA techniques (Section 2.2), and sampling for both PM2.5 and PM10 began. Overall 190 PM10 and 200 PM2.5 samples were collected onto Teflon filters from July 2008 to July 2012. Samples were collected on an alternating one-day-in-six (midnight to midnight) sampling regime for each of the size fractions so that a sample was collected every third day alternating between PM10 and PM2.5. Mass concentrations of PM10 and PM2.5 were determined gravimetrically. Field and lab blanks were
collected monthly and underwent the same analyses as loaded filters. As part of the quality control/quality assurance protocol, some samples were excluded because they were collected on the wrong side of the filter, were exposed twice, had no volumetric data available, or there was equipment failure. Overall, 24 PM10 and 22 PM2.5 filters were removed as part of the quality assurance process. In addition to the Partisol sampler, a continuous beta attenuation monitor (BAM; FH62, Thermo-Anderson) for PM10 concentrations and meteorological station were located at the sampling site. PM10 concentrations determined gravimetrically and by the BAM were in excellent agreement (r2 ¼ 0.98). The BC concentrations measured from the quartz fibre filters were comparable with those measured for the PM2.5 and PM10 Teflon filters (from timeseries analysis), so the datasets were combined to produce a BC time-series from 2006 to 2012. The extended PM10 and BC concentration datasets allowed us to perform trend analyses to investigate whether concentrations of either pollutant were decreasing over time. 2.2. Filter analysis IBA techniques were used to measure the concentrations of elements with atomic number above neon in the PM10 and PM2.5 samples collected on Teflon filters. The quartz fibre filters could not be analyzed using IBA techniques. IBA measurements for this study were carried out at the New Zealand Ion Beam Analysis Facility operated by the Institute of Geological and Nuclear Sciences (GNS) in Gracefield, Lower Hutt (Trompetter et al., 2005; Barry et al., 2012). The full suite of analyses included Particle-Induced X-ray Emission (PIXE), Particle-Induced Gamma-ray Emission (PIGE), Rutherford Backscattering (RBS) and Particle Elastic Scattering Analysis (PESA). Black carbon (BC) was measured using a M43D Digital Smoke Stain Reflectometer. The determination of BC concentrations from the reflectometer measurements has been reported previously (Ancelet et al., 2011). Elemental and BC concentrations were all below their respective limits of detection (LODs) on the lab and field blank filters. The IBA process provides analytical uncertainties and LODs for each element in each of the PM samples, with variations in these values dependent on the experimental conditions, filter matrix and sample loading. Both the analytical uncertainties and limits of detection are related to backgrounds present for each elemental peak. 2.3. Receptor modeling Receptor modeling and apportionment of PM mass by PMF was performed using the EPAPMF version 3.0.2.2 program in accordance with the User's Guide (USEPA, 2008). With PMF, sources are constrained to have non-negative species concentrations, no sample can have a negative source contribution and error estimates for each observed point are used as point-by-point weights. This is a distinct advantage of PMF, since it can accommodate missing or below detection limit data that is a common feature of environmental monitoring (Song et al., 2001). Prior to the PMF analyses, data and uncertainty matrices were prepared in the same manner as previous studies (Polissar et al., 1998; Song et al., 2001). Data screening and the source apportionment were performed in the same manner as previously reported (Polissar et al., 1998; Ancelet et al., 2012). 2.4. Trend analyses Trend analyses on PM10 and BC concentrations from 2006 to 2012 were performed using R statistical software and the TheilSen function of the openair package (Theil, 1950; Sen, 1968; R Development Core Team, 2011; Carslaw, 2012; Carslaw and
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Ropkins, 2012). Essentially, given a set of n x, y pairs, the Theil-Sen method calculates the slopes between all pairs of points and the median of these is the estimated slope over the whole dataset. Using the Theil-Sen method has a number of advantages, most notably its ability to yield accurate confidence intervals for nonnormal data and non-constant error variance (heteroscedasticity), and its resistance to outliers. More details about the Theil-Sen method can be found in the openair User Manual (Carslaw, 2012). Data were deseasonalized because of the effect that strong seasonal cycles can have on trend analyses. This is discussed in more detail in Section 3.2. 3. Results and discussion 3.1. Concentrations and sources of ambient PM10 and PM2.5 Fig. 1a and b present plots of 24-h average PM10 and PM2.5 concentrations, respectively, over the sampling period. From Fig. 1 a clear
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seasonal trend is apparent, with PM10 and PM2.5 concentrations peaking during the winter (JuneeAugust) when wood combustion for home heating is common (Trompetter et al., 2010; Ancelet et al., 2012; Davy et al., 2012). It is also clear from Fig. 1 that PM10 concentrations were driven by PM2.5 concentrations during the winter, where PM2.5 made up as much as 92% of the PM10 mass. Outside of winter, PM10 and PM2.5 concentrations were low, often less than 15 and 8 mg m3, respectively. Gaps present in Fig. 1a and b resulted from missed sample days or samples removed as part of the quality assurance process discussed in Section 2.1. Daily PM10 concentrations from the co-located BAM over the whole study period are presented in SM Fig. S2. From Fig. S2 it can be identified that PM10 concentrations exceeded the New Zealand NES 64 times during the sampling period. Elemental concentrations for PM10 and PM2.5 at St. Vincent Street are presented in Tables S1 and S2, respectively. Tables S1 and S2 indicate that some measured species were close to or below their LOD in each of the samples. Carbonaceous species, in this study represented by BC and H, were found to dominate PM2.5 mass
Fig. 1. Gravimetric PM10 (a) and PM 2.5 (b) concentrations (mg m3) for 24-h samples collected at the St. Vincent Street monitoring site. Gaps are from missed sampling days or filters removed as part of the quality assurance process.
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concentrations. Along with BC, other important elemental constituents included Cl, Fe, K, S and Si, and Cl, K, Na and S in PM10 and PM2.5, respectively. Using the elemental datasets, PMF identified five factors (sources) for each size fraction. The sources identified were biomass combustion, vehicles, secondary sulfate, marine aerosol and soil; and these sources were found to explain 99 and 93% of the PM10 and PM2.5 mass, respectively. The factor profiles for PM10 and PM2.5 are presented in Figs. 2 and 3, respectively. The first factor was characterized as biomass combustion because of the presence of H (an indicator of organic compounds), BC and K as primary species, along with some S and Cl, which is consistent with previous studies (Fine et al., 2002; Khalil and Rasmussen, 2003; Davy et al., 2012; Ancelet et al., 2014b). Arsenic was also present in the biomass combustion profile, which suggests that residents were burning copper chrome arsenate (CCA)-treated timber. We have previously reported similar results in other New Zealand locations, indicating that this behavior occurs nationwide (Ancelet et al., 2012, 2014c; Davy et al., 2012). Arsenic concentrations are discussed further in Section 3.3. Biomass burning contributions are from domestic wood combustion for home heating during the winter. Overall, biomass combustion accounted for 48 and 77% of PM10 and PM2.5 mass, respectively, over the sampling period. On peak pollution days however, biomass combustion could account for over 90% of the PM10 and PM2.5 mass. The second factor was identified as motor vehicles based on the presence of H, BC, Al, Si, Ca and Fe as significant elemental components. This profile represents both exhaust (tailpipe) emissions and non-exhaust (road dust and brake and tire wear) emissions. On
average, motor vehicles contributed 10 and 5% to PM10 and PM2.5 mass, respectively. The third and fourth factors were identified as secondary sulfate and marine aerosol, respectively. The sulfate factor profile featured S as the dominant elemental constituent, while the marine aerosol profile featured high concentrations of Na and Cl, along with Mg, S, K and Ca, all major constituents of seawater. The sulfate factor contributed 11 and 6% to the PM10 and PM2.5 mass, respectively, on average; while marine aerosol accounted for 18 and 7% of the PM10 and PM2.5 mass, respectively. The soil factor, identified based on the presence of crustal elements (Al, Si, Ca, Fe), accounted for 13 and 5% of the PM10 and PM2.5 mass, respectively. 3.1.1. Temporal variations Seasonal variations in source contributions to PM10 and PM2.5 are presented in Figs. S3 and S4, respectively. Biomass combustion contributions showed a clear seasonal trend, with peak concentrations occurring during winter. Some seasonality was apparent for sources other than biomass combustion in PM10 and PM2.5. In PM10, marine aerosol contributions were highest during spring and summer and vehicle contributions decreased during summer. Secondary sulfate contributions (PM10 and PM2.5) showed a slight seasonal trend, with peak contributions during spring and summer when conditions are favorable for the transformation of precursor gases. Motor vehicle contributions were slightly higher during winter, suggesting that meteorological conditions conducive to limited dispersion and pollutant build-up played an important role.
Fig. 2. PM10 factor profiles at St. Vincent Street.
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Fig. 3. PM2.5 factor profiles at St. Vincent Street.
Average mass contributions from the different sources were also divided into weekday (136 PM10 and 138 PM2.5) and weekend (53 PM10 and 60 PM2.5) categories to examine any differences in relative contributions. Figs. S5 and S6 present plots of weekday and weekend source mass contributions to PM10 and PM2.5, respectively, over the monitoring period. For PM10, three sources showed significantly higher contributions during weekdays compared to weekends. The sources were vehicles, soil and secondary sulfate. In PM2.5, vehicles and secondary sulfate also had significantly higher concentrations during weekdays. Motor vehicle contributions would be expected to be higher during weekdays because of commuter traffic with an associated increase in motor vehicle emissions. The higher weekday contribution from soil in PM10 suggests that re-entrainment by motor vehicles and/or local commercial activities such as construction or excavation could have an important influence on soil contributions. The cause for higher weekday contributions from secondary sulfate in PM10 and PM2.5 is unclear, but could be related to shipping activity at the Port of Nelson (Ancelet et al., 2014b). More work would be required to confirm this suggestion. 3.2. Trends in PM10 and BC concentrations Because PM sampling onto quartz fibre filters began in Nelson in 2006, a large dataset (2006e2012) was available to analyze trends in PM10 and BC concentrations. Analyzing trends in BC concentrations is important because of its role in atmospheric warming and its effects on human health (Janssen et al., 2012; Bond et al., 2013). As discussed in Section 2.4, the TheilSen function in the openair package was used for the trend analyses (Carslaw, 2012; Carslaw and Ropkins, 2012). Strong seasonal cycles for both PM10 and BC concentrations can affect the results of the trend analysis because it is not only the quantity and rate of emissions that dictate ambient concentrations, but meteorology can also have significant influences on local pollutant concentrations (Trompetter et al., 2010). As such, the data were deseasonalized for the analyses using the “deaseasonalize” capability of the openair package. When the data were deseasonalized, decreasing trends were evident, with PM10
decreasing at an average rate of 0.5 mg m3 per year (90% confidence limits) and BC decreasing at 100 ng m3 per year (95% confidence limits), as shown in Fig. 4a and b, respectively. We suggest that the remaining 400 ng m3 decrease in PM10 concentrations is related to decreasing organics emitted through biomass combustion. This is because while the composition of the vehicle fleet in Nelson has not changed much, a new woodburner standard, that specifies emissions must be lower than 1 g kg1, were introduced in 2006. As households have replaced their old woodburners with newer, more efficient ones, emissions within the airshed have decreased. The trends in PM10 and BC were explored further by examining the average seasonal trends, which show (Fig. S7) that much of the decrease in concentrations for PM10 (3 mg m3 per year, 95% confidence limits) and BC (242 ng m3 per year, 95% confidence limits) has occurred during winter months (June, July and August). Interestingly, the data suggests that there has also been a strongly significant (99.9% confidence limits) decrease in summer BC concentrations and this may reflect the introduction of outdoor burning restrictions in the Nelson airshed and further afield. These restrictions were introduced in 2003 for Nelson city, and have slowly been adopted by surrounding communities. The restrictions completely ban outdoor burning in the city, and severely limit burning outside the city. Outdoor burning was common around Nelson, largely because of the semi-rural nature of its surrounding areas. Many of these areas burned large amounts of biomass during the summer. 3.3. Arsenic associated with PM10 and PM2.5 The receptor modeling results for PM10 and PM2.5 samples collected at the St. Vincent Street site, discussed in Section 3.1, showed that arsenic was strongly associated with the biomass combustion source because of the use of copper chrome arsenate (CCA)-treated timber as part of the fuel stream in domestic fires. The data here, and from other studies throughout New Zealand (Ancelet et al., 2012, 2014c; Davy et al., 2012), indicate that the As is confined to the PM2.5 size fraction. The As elemental concentration results for the PM2.5 and PM10 Teflon filters measured using IBA
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Fig. 4. Trend analysis for PM10 (a) and BC (b) deseasonalized concentrations at the St Vincent Street site. The solid black line indicates the trend estimate, while the dashed gray lines indicate the 95% confidence intervals based on data resampling methods.
have therefore been combined to produce an As time-series from 2008 to 2012 (Fig. 5). A clear seasonal trend is apparent from Fig. 5, with peak As concentrations occurring during winter. Arsenic associated with air particulate matter pollution is primarily from the combustion of arsenic-containing fuels, such as coal and CCA-treated timber. Research on emissions from coal-fired power plants indicates that arsenic is released as particle associated arsenic oxides, mostly as the fully oxidised arsenate (As in 5þ oxidation state) (Shah et al., 2006). Combustion of treated timber in wood burners (or open burning) is at lower temperatures compared to coal fired power plants (Helsen and van den Bulck, 2003; Shah et al., 2006). Previous work has suggested that both the 3þ and 4þ oxidation states are released under lower burning temperatures, and that low temperature pyrolosis (<327 C) may retain arsenic in the ash (Helsen and van den Bulck, 2003). Helsen and van den Bulck (2003) also found that copper and chromium are preferentially retained in the ash when CCA-treated timber is burned. New Zealand has ambient air quality guidelines for arsenic species. The guideline value for inorganic arsenic is 5.5 ng m3 (annual average) and for arsine (AsH3) the guideline value is 55 ng m3 (annual average). At temperatures above 230 C arsine
decomposes to arsenic oxides (Lide, 1992), therefore arsine is unlikely to be present in combustion emissions. As such, we assumed that all arsenic measured using IBA was present as inorganic oxides. Using data from 2009 to 2011, when data collected over the whole year was available, we calculated the annual average As concentrations. The calculated annual averages are presented in Fig. S8. From Fig. S8 it is clear that As concentrations substantially exceed the annual average guideline, with the annual average value in 2009 more than three times the guideline value (16.1 ng m3). The calculated annual averages in 2010 and 2011 were 10.4 and 11.4 ng m3, respectively. The use of CCA-treated timber as fuel for domestic fires is probably widespread in New Zealand urban areas but only as and when waste timber is at hand. As such, we do not yet have a strong understanding of how much CCA-treated timber is actually burned as a percentage of the household fuel stream. Understanding how much CCA-treated timber is burned is the focus of ongoing investigation in our research group. The data suggests that sufficient quantities are being burned to have an acute localised effect, but repeated exposure year-to-year during winter may also include a chronic exposure that is close to or exceeds ambient air quality guidelines at urban locations in New Zealand. The problem
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Fig. 5. Arsenic concentrations (ng m3) at the St. Vincent Street monitoring site.
presents itself as one of enforcement of air quality regulations or a need for more extensive public education to encourage more awareness of their wood burning practices. A further issue is the disposal of the ash from domestic fires that is likely to be contaminated with residual arsenic, as well as copper and chromium. If this is used in gardens it may pose an addition exposure pathway through the ingestion of any vegetables grown in contaminated soils.
considered to be from the use of CCA-treated timber as fuel for domestic fires. The use of CCA-treated timber as fuel for domestic fires is probably widespread in New Zealand urban areas, but only as and when waste timber is at hand. The data suggests that sufficient quantities are being burned to have an acute localised effect, but repeated exposure year-to-year during winter may also include chronic exposure. Conflict of interest
4. Conclusions The authors declare no conflicts of interest. Elemental analysis and source apportionment of PM10 and PM2.5 samples from Nelson collected from 2008 to 2012 has provided important information for managing air quality in the city. Five sources were identified for both size fractions: biomass combustion, motor vehicles, secondary sulfate, marine aerosol and soil. The biomass combustion source had a strong seasonal cycle, with peak concentrations occurring in winter when many residents burn wood for home heating. Little seasonality was apparent in the four other sources. Motor vehicle and secondary sulfate contributions were higher during weekdays than weekends. Although the higher weekday contributions from motor vehicles was expected based on commuter behavior and traffic density, the reason for the higher secondary sulfate contributions during weekdays remains unclear. It is possible that activities around the port of Nelson could have had an impact, but more work is required to confirm this. A seven year record (2006e2012) of black carbon concentrations provided sufficient data trend analyses to be performed. We found that black carbon and PM10 concentrations were decreasing year-on-year, with the primary decrease occurring during winter months at a rate of 242 ng m3/winter/year for black carbon, and 3 mg m3/winter/year for PM10. Because biomass combustion is the predominant source of BC during the winter, it is likely these reductions are related to decreased wood burning emissions. Continued reductions in wood combustion emissions, through phasing-out old burners or other policy measures, would significantly improve air quality in Nelson. Arsenic contamination in particulate matter has been found in urban air across New Zealand and Nelson was no exception. The New Zealand Ambient Air Quality Guideline for arsenic (5.5 ng m3 annual average) was substantially exceeded every year, with the highest concentrations measured during winter (maxima around 90 ng m3). The arsenic was strongly associated with the biomass combustion source and therefore the arsenic contamination was
Acknowledgments The authors thank Nelson City Council and the New Zealand Ministry of Business, Innovation and Employment (Envirolink grants 1273-NLCC 71 and 1291-NLCC72) for funding. Support from Paul Sheldon (NCC) was greatly appreciated. The authors also thank Chris Purcell for operating and maintaining the 3 MV accelerator used for the IBA analyses. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.apr.2015.06.008. References Ancelet, T., Davy, P.K., Trompetter, W.J., Markwitz, A., Weatherburn, D.C., 2011. Carbonaceous aerosols in an urban tunnel. Atmos. Environ. 45, 4463e4469. http://dx.doi.org/10.1016/j.atmosenv.2011.05.032. Ancelet, T., Davy, P.K., Mitchell, T., Trompetter, W.J., Markwitz, A., Weatherburn, D.C., 2012. Identification of particulate matter sources on an hourly time-scale in a wood burning community. Environ. Sci. Technol. 46, 4767e4774. http:// dx.doi.org/10.1021/es203937y. Ancelet, T., Davy, P.K., Trompetter, W.J., Markwitz, A., 2014a. Sources of particulate matter pollution in a small New Zealand city. Atmos. Pollut. Res. 5, 572e580. http://dx.doi.org/10.5094/APR.2014.066. Ancelet, T., Davy, P.K., Trompetter, W.J., Markwitz, A., 2014b. Sources and transport of particulate matter on an hourly time-scale during the winter in a New Zealand urban valley. Urban Clim. 10, 644e655. Ancelet, T., Davy, P.K., Trompetter, W.J., Markwitz, A., Weatherburn, D.C., 2014c. Particulate matter sources on an hourly timescale in a rural community during the winter. J. Air Waste Manag. Assoc. 64, 501e508. http://dx.doi.org/10.1080/ 10962247.2013.813414. Barry, B., Trompetter, W.J., Davy, P.K., Markwitz, A., 2012. Recent developments in the air particulate research capability at the New Zealand ion beam analysis facility. Int. J. PIXE 22, 121e130. http://dx.doi.org/10.1142/S012908351240013X.
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Paatero, P., 1999. The multilinear engineda table-driven, least squares program for solving multilinear problems, including the n-way parallel factor analysis model. J. Comput. Graph. Stat. 8, 854e888. Paatero, P., Tapper, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111e126. Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F., 1998. Atmospheric aerosol over Alaska: 2. Elemental composition and sources. J. Geophys. Res. Atmos. 103, 19045e19057. Pope III, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manag. Assoc. 56, 709e742. http://dx.doi.org/ 10.1080/10473289.2006.10464485. sfai, M., Buseck, P.R., 2010. Nature and climate effects of individual tropospheric Po aerosol particles. Annu. Rev. Earth Planet. Sci. 38, 17e43. http://dx.doi.org/ 10.1146/annurev.earth.031208.100032. €schl, U., 2005. Atmospheric aerosols: composition, transformation, climate and Po health effects. Angew. Chem. Int. Ed. 44, 7520e7540. http://dx.doi.org/10.1002/ anie.200501122. R Development Core Team, 2011. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Samet, J.M., Dominici, F., Curriero, F.C., Coursac, I., Zeger, S.L., 2000. Fine particulate air pollution and mortality in 20 US cities,1987e1994. N. Engl. J. Med. 343,1742e1749. Sen, P.K., 1968. Estimates of regression coefficient based on Kendall's tau. J. Am. Stat. Assoc. 63, 1379e1389. Shah, P., Strezov, V., Stevanov, C., Nelson, P.F., 2006. Speciation of arsenic and Selenium in coal combustion products. Energy & Fuels 21, 506e512. Song, X.-H., Polissar, A.V., Hopke, P.K., 2001. Sources of fine particle composition in the northeastern US. Atmos. Environ. 35, 5277e5286. Staniswalis, J.G., Parks, N.J., Bader, J.O., Maldonado, Y.M., 2005. Temporal analysis of airborne particulate matter reveals a dose-rate effect on mortality in El Paso: indications of differential toxicity for different particle mixtures. J. Air Waste Manag. Assoc. 55, 893e902. http://dx.doi.org/10.1080/10473289.2005.10464696. Theil, H., 1950. A rank invariant method of linear and polynomial regression analysis, I, II, III. In: Proceedings of the Koninklijke Nederlandse Akademie Wetenschappen. Series Aemathematical Sciences, vol. 53, 386e392, 521e525, 1397e1412. Trompetter, W.J., Markwitz, A., Davy, P., 2005. Air particulate research capability at the New Zealand ion beam analysis facility using PIXE and IBA techniques. Int. J. PIXE 15, 249e255. Trompetter, W.J., Davy, P.K., Markwitz, A., 2010. Influence of environmental conditions on carbonaceous particle concentrations within New Zealand. J. Aerosol Sci. 41, 134e142. http://dx.doi.org/10.1016/j.jaerosci.2009.11.003. Trompetter, W.J., Grange, S.K., Davy, P.K., Ancelet, T., 2013. Vertical and temporal variations of black carbon in New Zealand urban areas during winter. Atmos. Environ. 75, 179e187. U. S. Environmental Protection Agency (USEPA), 2008. EPA Positive Matrix Factorization (PMF) 3.0 Fundamentals and User Guide. USEPA National Exposure Research Laboratory, Research Triangle Park, NC. U. S. Environmental Protection Agency (USEPA), 2014. EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. USEPA National Exposure Research Laboratory, Research Triangle Park, NC.
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Original article
Particulate matter sources and long-term trends in a small New Zealand city Travis Ancelet*, Perry K. Davy, William J. Trompetter GNS Science, 30 Gracefield Road, PO Box 31312, Lower Hutt, New Zealand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 March 2015 Received in revised form 14 June 2015 Accepted 15 June 2015 Available online 12 October 2015
Particulate matter samples (PM10 and PM2.5) in downtown Nelson, New Zealand were collected from 2006 to 2012. These samples were used to investigate sources of PM10 and PM2.5, and to evaluate longterm trends in PM10 and BC concentrations. Five PM10 and PM2.5 sources were identified using positive matrix factorization: biomass combustion, motor vehicles, secondary sulfate, marine aerosol and soil. Overall, biomass combustion was the dominant contributor to PM10 (48%) and PM2.5 (77%) mass. The biomass combustion factor profile featured arsenic, suggesting that locals were burning copper chrome arsenate-treated timber, an activity that appears to occur throughout New Zealand. Trend analyses on PM10 and black carbon concentrations revealed that both were decreasing year-onyear, at an average rate of 0.5 mg m3 per year and 100 ng m3 per year, respectively. This study provides important information for Nelson City Council, who are responsible for managing air quality in Nelson, to effectively manage air quality. This study also shows that relatively simple mitigation measures can instigate decreases in PM and BC concentrations. Copyright © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: Particulate matter Source apportionment Trend analysis Biomass burning Arsenic
1. Introduction It is well-established that particulate matter (PM) has adverse effects on human health and the environment (Dockery et al., 1993; € schl, 2005; Pope and Dockery, Samet et al., 2000; Nel, 2005; Po sfai and Buseck, 2010). The International Agency for 2006; Po Research on Cancer (IARC) has even declared that outdoor pollution, and specifically PM, is carcinogenic to humans (IARC, 2013). Because of these effects, PM concentrations are routinely monitored worldwide and managed according to local legislation. In New Zealand, the National Environmental Standard (NES) for PM sets a 24-h average limit for PM10 (particulate matter with aerodynamic diameters less than 10 mm) concentrations at 50 mg m3 that can only be exceeded once per year. Many urban areas in New Zealand exceed the NES numerous times each year, particularly during the winter when wood combustion for home heating is common. No standard exists for PM2.5 in New Zealand. The myriad of adverse health and environmental effects associated with PM are
* Corresponding author. Tel.: þ64 4 570 4668; fax: þ64 4 570 4657. E-mail address: [email protected] (T. Ancelet). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
related to a number of factors, including particle size, composition and surface area (Hannigan et al., 2005; Staniswalis et al., 2005). As such, size-resolved information about PM sources and their contributions to ambient PM concentrations is critical for effective air quality management. To identify the sources contributing to measured PM concentrations, multivariate receptor models are used. Positive matrix factorization (PMF) is a powerful and commonly used multivariate receptor model that is capable of resolving factors, or PM sources, without prior source knowledge. It is however, important to note that source-specific profiles (fingerprints) must be known to properly assign the PMF model outputs. PMF has a number of advantages over traditional factor analysis techniques including nonnegativity constraints and the ability to accommodate missing or below detection limit data. The results of the analysis are directly interpretable as mass contributions from each factor (Paatero and Tapper, 1994; Paatero, 1997; Song et al., 2001). Two receptor models are available to perform PMF, PMF2 (Paatero, 1997) and EPA PMF (USEPA, 2008; USEPA, 2014). EPA PMF incorporates a graphical user interface and a bilinear model that is solved by the Multilinear Engine (Paatero, 1999). Previous studies have shown that EPA PMF and PMF2 produce similar results, with only minor differences in the final solutions (Kim and Hopke, 2007; Hwang and Hopke, 2011).
http://dx.doi.org/10.1016/j.apr.2015.06.008 1309-1042/Copyright © 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
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Nelson, located on the northern coast of New Zealand's South Island (latitude 41.16 , longitude 173.17 ), is a small city with a population of approximately 43 000. Nelson is known to suffer from poor air quality during the winter when wood burning for domestic heating and strong temperature inversions that limit the dispersion of pollutants are common (Trompetter et al., 2013). These conditions cause PM10 concentrations to exceed the New Zealand NES a number of times each year, which has important implications for Nelson City Council (NCC), the local body responsible for managing air pollution, because the NES cannot be exceeded more than once per year. A previous study using PMF in the Nelson suburb of Tahunanui showed that during the winter, exceedances of the NES were driven by domestic wood combustion, while exceedances during the spring and summer were the result of vehicular movements on unsealed roads (Ancelet et al., 2014a). Little is known about PM sources and their contributions in Nelson city itself; and nothing is known about the effectiveness of policy measures, including the implementation of stringent wood combustion appliance standards and educational campaigns, taken by NCC to reduce wintertime PM emissions from domestic heating. In this study we aimed to address this knowledge gap using PM2.5 and PM10 samples collected from 2008 to 2012. We also used a larger dataset of PM10 and BC concentrations to understand trends in these two pollutants. Understanding trends in PM10 and BC concentrations is critical because it highlights how effective air quality management strategies are, and whether there may be more costeffective options for reducing pollutants. Trends in BC concentrations, in particular, show how combustion emissions change over time, which is critical for many locations around the world and can have important implications for Earth's climate (Bond et al., 2013). Our approach of using BC concentrations from quartz fibre filters in combination with those from Teflon filters provides a way for researchers and air quality managers to use historical samples for measuring progress on air quality improvements. 2. Materials and methods 2.1. Sample collection and site description Particulate matter samples (PM10 and PM2.5) were collected over 24-h at an ambient air quality monitoring station located on a property off of St. Vincent Street, Nelson (Lat: 41.164150 , Long: 173.162447, elevation: 5 m). NCC were responsible for all filter changes and equipment maintenance. Fig. S1 of the Supplementary Material (SM) presents a map of New Zealand and the site location on a map of the local area. St. Vincent Street is located near (within 600 m) the Nelson central business district. The site was approximately 90 m from the nearest road and surrounded by open space or buildings no more than two stories high. Sampling began in January 2006 and continued until July 2012. From 2006 to 2008, PM10 samples at the St. Vincent Street site were collected onto quartz fibre filters using a Partisol sampler (Thermo Scientific, Waltham, MA, USA). While these samples could not be analyzed for their elemental content using IBA techniques, BC concentrations for each of the samples were determined. In total, 238 samples were collected onto quartz fibre filters from January 2006 to April 2008. In July 2008 the filter material was changed to Teflon so that the samples could be analyzed using IBA techniques (Section 2.2), and sampling for both PM2.5 and PM10 began. Overall 190 PM10 and 200 PM2.5 samples were collected onto Teflon filters from July 2008 to July 2012. Samples were collected on an alternating one-day-in-six (midnight to midnight) sampling regime for each of the size fractions so that a sample was collected every third day alternating between PM10 and PM2.5. Mass concentrations of PM10 and PM2.5 were determined gravimetrically. Field and lab blanks were
collected monthly and underwent the same analyses as loaded filters. As part of the quality control/quality assurance protocol, some samples were excluded because they were collected on the wrong side of the filter, were exposed twice, had no volumetric data available, or there was equipment failure. Overall, 24 PM10 and 22 PM2.5 filters were removed as part of the quality assurance process. In addition to the Partisol sampler, a continuous beta attenuation monitor (BAM; FH62, Thermo-Anderson) for PM10 concentrations and meteorological station were located at the sampling site. PM10 concentrations determined gravimetrically and by the BAM were in excellent agreement (r2 ¼ 0.98). The BC concentrations measured from the quartz fibre filters were comparable with those measured for the PM2.5 and PM10 Teflon filters (from timeseries analysis), so the datasets were combined to produce a BC time-series from 2006 to 2012. The extended PM10 and BC concentration datasets allowed us to perform trend analyses to investigate whether concentrations of either pollutant were decreasing over time. 2.2. Filter analysis IBA techniques were used to measure the concentrations of elements with atomic number above neon in the PM10 and PM2.5 samples collected on Teflon filters. The quartz fibre filters could not be analyzed using IBA techniques. IBA measurements for this study were carried out at the New Zealand Ion Beam Analysis Facility operated by the Institute of Geological and Nuclear Sciences (GNS) in Gracefield, Lower Hutt (Trompetter et al., 2005; Barry et al., 2012). The full suite of analyses included Particle-Induced X-ray Emission (PIXE), Particle-Induced Gamma-ray Emission (PIGE), Rutherford Backscattering (RBS) and Particle Elastic Scattering Analysis (PESA). Black carbon (BC) was measured using a M43D Digital Smoke Stain Reflectometer. The determination of BC concentrations from the reflectometer measurements has been reported previously (Ancelet et al., 2011). Elemental and BC concentrations were all below their respective limits of detection (LODs) on the lab and field blank filters. The IBA process provides analytical uncertainties and LODs for each element in each of the PM samples, with variations in these values dependent on the experimental conditions, filter matrix and sample loading. Both the analytical uncertainties and limits of detection are related to backgrounds present for each elemental peak. 2.3. Receptor modeling Receptor modeling and apportionment of PM mass by PMF was performed using the EPAPMF version 3.0.2.2 program in accordance with the User's Guide (USEPA, 2008). With PMF, sources are constrained to have non-negative species concentrations, no sample can have a negative source contribution and error estimates for each observed point are used as point-by-point weights. This is a distinct advantage of PMF, since it can accommodate missing or below detection limit data that is a common feature of environmental monitoring (Song et al., 2001). Prior to the PMF analyses, data and uncertainty matrices were prepared in the same manner as previous studies (Polissar et al., 1998; Song et al., 2001). Data screening and the source apportionment were performed in the same manner as previously reported (Polissar et al., 1998; Ancelet et al., 2012). 2.4. Trend analyses Trend analyses on PM10 and BC concentrations from 2006 to 2012 were performed using R statistical software and the TheilSen function of the openair package (Theil, 1950; Sen, 1968; R Development Core Team, 2011; Carslaw, 2012; Carslaw and
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Ropkins, 2012). Essentially, given a set of n x, y pairs, the Theil-Sen method calculates the slopes between all pairs of points and the median of these is the estimated slope over the whole dataset. Using the Theil-Sen method has a number of advantages, most notably its ability to yield accurate confidence intervals for nonnormal data and non-constant error variance (heteroscedasticity), and its resistance to outliers. More details about the Theil-Sen method can be found in the openair User Manual (Carslaw, 2012). Data were deseasonalized because of the effect that strong seasonal cycles can have on trend analyses. This is discussed in more detail in Section 3.2. 3. Results and discussion 3.1. Concentrations and sources of ambient PM10 and PM2.5 Fig. 1a and b present plots of 24-h average PM10 and PM2.5 concentrations, respectively, over the sampling period. From Fig. 1 a clear
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seasonal trend is apparent, with PM10 and PM2.5 concentrations peaking during the winter (JuneeAugust) when wood combustion for home heating is common (Trompetter et al., 2010; Ancelet et al., 2012; Davy et al., 2012). It is also clear from Fig. 1 that PM10 concentrations were driven by PM2.5 concentrations during the winter, where PM2.5 made up as much as 92% of the PM10 mass. Outside of winter, PM10 and PM2.5 concentrations were low, often less than 15 and 8 mg m3, respectively. Gaps present in Fig. 1a and b resulted from missed sample days or samples removed as part of the quality assurance process discussed in Section 2.1. Daily PM10 concentrations from the co-located BAM over the whole study period are presented in SM Fig. S2. From Fig. S2 it can be identified that PM10 concentrations exceeded the New Zealand NES 64 times during the sampling period. Elemental concentrations for PM10 and PM2.5 at St. Vincent Street are presented in Tables S1 and S2, respectively. Tables S1 and S2 indicate that some measured species were close to or below their LOD in each of the samples. Carbonaceous species, in this study represented by BC and H, were found to dominate PM2.5 mass
Fig. 1. Gravimetric PM10 (a) and PM 2.5 (b) concentrations (mg m3) for 24-h samples collected at the St. Vincent Street monitoring site. Gaps are from missed sampling days or filters removed as part of the quality assurance process.
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concentrations. Along with BC, other important elemental constituents included Cl, Fe, K, S and Si, and Cl, K, Na and S in PM10 and PM2.5, respectively. Using the elemental datasets, PMF identified five factors (sources) for each size fraction. The sources identified were biomass combustion, vehicles, secondary sulfate, marine aerosol and soil; and these sources were found to explain 99 and 93% of the PM10 and PM2.5 mass, respectively. The factor profiles for PM10 and PM2.5 are presented in Figs. 2 and 3, respectively. The first factor was characterized as biomass combustion because of the presence of H (an indicator of organic compounds), BC and K as primary species, along with some S and Cl, which is consistent with previous studies (Fine et al., 2002; Khalil and Rasmussen, 2003; Davy et al., 2012; Ancelet et al., 2014b). Arsenic was also present in the biomass combustion profile, which suggests that residents were burning copper chrome arsenate (CCA)-treated timber. We have previously reported similar results in other New Zealand locations, indicating that this behavior occurs nationwide (Ancelet et al., 2012, 2014c; Davy et al., 2012). Arsenic concentrations are discussed further in Section 3.3. Biomass burning contributions are from domestic wood combustion for home heating during the winter. Overall, biomass combustion accounted for 48 and 77% of PM10 and PM2.5 mass, respectively, over the sampling period. On peak pollution days however, biomass combustion could account for over 90% of the PM10 and PM2.5 mass. The second factor was identified as motor vehicles based on the presence of H, BC, Al, Si, Ca and Fe as significant elemental components. This profile represents both exhaust (tailpipe) emissions and non-exhaust (road dust and brake and tire wear) emissions. On
average, motor vehicles contributed 10 and 5% to PM10 and PM2.5 mass, respectively. The third and fourth factors were identified as secondary sulfate and marine aerosol, respectively. The sulfate factor profile featured S as the dominant elemental constituent, while the marine aerosol profile featured high concentrations of Na and Cl, along with Mg, S, K and Ca, all major constituents of seawater. The sulfate factor contributed 11 and 6% to the PM10 and PM2.5 mass, respectively, on average; while marine aerosol accounted for 18 and 7% of the PM10 and PM2.5 mass, respectively. The soil factor, identified based on the presence of crustal elements (Al, Si, Ca, Fe), accounted for 13 and 5% of the PM10 and PM2.5 mass, respectively. 3.1.1. Temporal variations Seasonal variations in source contributions to PM10 and PM2.5 are presented in Figs. S3 and S4, respectively. Biomass combustion contributions showed a clear seasonal trend, with peak concentrations occurring during winter. Some seasonality was apparent for sources other than biomass combustion in PM10 and PM2.5. In PM10, marine aerosol contributions were highest during spring and summer and vehicle contributions decreased during summer. Secondary sulfate contributions (PM10 and PM2.5) showed a slight seasonal trend, with peak contributions during spring and summer when conditions are favorable for the transformation of precursor gases. Motor vehicle contributions were slightly higher during winter, suggesting that meteorological conditions conducive to limited dispersion and pollutant build-up played an important role.
Fig. 2. PM10 factor profiles at St. Vincent Street.
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Fig. 3. PM2.5 factor profiles at St. Vincent Street.
Average mass contributions from the different sources were also divided into weekday (136 PM10 and 138 PM2.5) and weekend (53 PM10 and 60 PM2.5) categories to examine any differences in relative contributions. Figs. S5 and S6 present plots of weekday and weekend source mass contributions to PM10 and PM2.5, respectively, over the monitoring period. For PM10, three sources showed significantly higher contributions during weekdays compared to weekends. The sources were vehicles, soil and secondary sulfate. In PM2.5, vehicles and secondary sulfate also had significantly higher concentrations during weekdays. Motor vehicle contributions would be expected to be higher during weekdays because of commuter traffic with an associated increase in motor vehicle emissions. The higher weekday contribution from soil in PM10 suggests that re-entrainment by motor vehicles and/or local commercial activities such as construction or excavation could have an important influence on soil contributions. The cause for higher weekday contributions from secondary sulfate in PM10 and PM2.5 is unclear, but could be related to shipping activity at the Port of Nelson (Ancelet et al., 2014b). More work would be required to confirm this suggestion. 3.2. Trends in PM10 and BC concentrations Because PM sampling onto quartz fibre filters began in Nelson in 2006, a large dataset (2006e2012) was available to analyze trends in PM10 and BC concentrations. Analyzing trends in BC concentrations is important because of its role in atmospheric warming and its effects on human health (Janssen et al., 2012; Bond et al., 2013). As discussed in Section 2.4, the TheilSen function in the openair package was used for the trend analyses (Carslaw, 2012; Carslaw and Ropkins, 2012). Strong seasonal cycles for both PM10 and BC concentrations can affect the results of the trend analysis because it is not only the quantity and rate of emissions that dictate ambient concentrations, but meteorology can also have significant influences on local pollutant concentrations (Trompetter et al., 2010). As such, the data were deseasonalized for the analyses using the “deaseasonalize” capability of the openair package. When the data were deseasonalized, decreasing trends were evident, with PM10
decreasing at an average rate of 0.5 mg m3 per year (90% confidence limits) and BC decreasing at 100 ng m3 per year (95% confidence limits), as shown in Fig. 4a and b, respectively. We suggest that the remaining 400 ng m3 decrease in PM10 concentrations is related to decreasing organics emitted through biomass combustion. This is because while the composition of the vehicle fleet in Nelson has not changed much, a new woodburner standard, that specifies emissions must be lower than 1 g kg1, were introduced in 2006. As households have replaced their old woodburners with newer, more efficient ones, emissions within the airshed have decreased. The trends in PM10 and BC were explored further by examining the average seasonal trends, which show (Fig. S7) that much of the decrease in concentrations for PM10 (3 mg m3 per year, 95% confidence limits) and BC (242 ng m3 per year, 95% confidence limits) has occurred during winter months (June, July and August). Interestingly, the data suggests that there has also been a strongly significant (99.9% confidence limits) decrease in summer BC concentrations and this may reflect the introduction of outdoor burning restrictions in the Nelson airshed and further afield. These restrictions were introduced in 2003 for Nelson city, and have slowly been adopted by surrounding communities. The restrictions completely ban outdoor burning in the city, and severely limit burning outside the city. Outdoor burning was common around Nelson, largely because of the semi-rural nature of its surrounding areas. Many of these areas burned large amounts of biomass during the summer. 3.3. Arsenic associated with PM10 and PM2.5 The receptor modeling results for PM10 and PM2.5 samples collected at the St. Vincent Street site, discussed in Section 3.1, showed that arsenic was strongly associated with the biomass combustion source because of the use of copper chrome arsenate (CCA)-treated timber as part of the fuel stream in domestic fires. The data here, and from other studies throughout New Zealand (Ancelet et al., 2012, 2014c; Davy et al., 2012), indicate that the As is confined to the PM2.5 size fraction. The As elemental concentration results for the PM2.5 and PM10 Teflon filters measured using IBA
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Fig. 4. Trend analysis for PM10 (a) and BC (b) deseasonalized concentrations at the St Vincent Street site. The solid black line indicates the trend estimate, while the dashed gray lines indicate the 95% confidence intervals based on data resampling methods.
have therefore been combined to produce an As time-series from 2008 to 2012 (Fig. 5). A clear seasonal trend is apparent from Fig. 5, with peak As concentrations occurring during winter. Arsenic associated with air particulate matter pollution is primarily from the combustion of arsenic-containing fuels, such as coal and CCA-treated timber. Research on emissions from coal-fired power plants indicates that arsenic is released as particle associated arsenic oxides, mostly as the fully oxidised arsenate (As in 5þ oxidation state) (Shah et al., 2006). Combustion of treated timber in wood burners (or open burning) is at lower temperatures compared to coal fired power plants (Helsen and van den Bulck, 2003; Shah et al., 2006). Previous work has suggested that both the 3þ and 4þ oxidation states are released under lower burning temperatures, and that low temperature pyrolosis (<327 C) may retain arsenic in the ash (Helsen and van den Bulck, 2003). Helsen and van den Bulck (2003) also found that copper and chromium are preferentially retained in the ash when CCA-treated timber is burned. New Zealand has ambient air quality guidelines for arsenic species. The guideline value for inorganic arsenic is 5.5 ng m3 (annual average) and for arsine (AsH3) the guideline value is 55 ng m3 (annual average). At temperatures above 230 C arsine
decomposes to arsenic oxides (Lide, 1992), therefore arsine is unlikely to be present in combustion emissions. As such, we assumed that all arsenic measured using IBA was present as inorganic oxides. Using data from 2009 to 2011, when data collected over the whole year was available, we calculated the annual average As concentrations. The calculated annual averages are presented in Fig. S8. From Fig. S8 it is clear that As concentrations substantially exceed the annual average guideline, with the annual average value in 2009 more than three times the guideline value (16.1 ng m3). The calculated annual averages in 2010 and 2011 were 10.4 and 11.4 ng m3, respectively. The use of CCA-treated timber as fuel for domestic fires is probably widespread in New Zealand urban areas but only as and when waste timber is at hand. As such, we do not yet have a strong understanding of how much CCA-treated timber is actually burned as a percentage of the household fuel stream. Understanding how much CCA-treated timber is burned is the focus of ongoing investigation in our research group. The data suggests that sufficient quantities are being burned to have an acute localised effect, but repeated exposure year-to-year during winter may also include a chronic exposure that is close to or exceeds ambient air quality guidelines at urban locations in New Zealand. The problem
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Fig. 5. Arsenic concentrations (ng m3) at the St. Vincent Street monitoring site.
presents itself as one of enforcement of air quality regulations or a need for more extensive public education to encourage more awareness of their wood burning practices. A further issue is the disposal of the ash from domestic fires that is likely to be contaminated with residual arsenic, as well as copper and chromium. If this is used in gardens it may pose an addition exposure pathway through the ingestion of any vegetables grown in contaminated soils.
considered to be from the use of CCA-treated timber as fuel for domestic fires. The use of CCA-treated timber as fuel for domestic fires is probably widespread in New Zealand urban areas, but only as and when waste timber is at hand. The data suggests that sufficient quantities are being burned to have an acute localised effect, but repeated exposure year-to-year during winter may also include chronic exposure. Conflict of interest
4. Conclusions The authors declare no conflicts of interest. Elemental analysis and source apportionment of PM10 and PM2.5 samples from Nelson collected from 2008 to 2012 has provided important information for managing air quality in the city. Five sources were identified for both size fractions: biomass combustion, motor vehicles, secondary sulfate, marine aerosol and soil. The biomass combustion source had a strong seasonal cycle, with peak concentrations occurring in winter when many residents burn wood for home heating. Little seasonality was apparent in the four other sources. Motor vehicle and secondary sulfate contributions were higher during weekdays than weekends. Although the higher weekday contributions from motor vehicles was expected based on commuter behavior and traffic density, the reason for the higher secondary sulfate contributions during weekdays remains unclear. It is possible that activities around the port of Nelson could have had an impact, but more work is required to confirm this. A seven year record (2006e2012) of black carbon concentrations provided sufficient data trend analyses to be performed. We found that black carbon and PM10 concentrations were decreasing year-on-year, with the primary decrease occurring during winter months at a rate of 242 ng m3/winter/year for black carbon, and 3 mg m3/winter/year for PM10. Because biomass combustion is the predominant source of BC during the winter, it is likely these reductions are related to decreased wood burning emissions. Continued reductions in wood combustion emissions, through phasing-out old burners or other policy measures, would significantly improve air quality in Nelson. Arsenic contamination in particulate matter has been found in urban air across New Zealand and Nelson was no exception. The New Zealand Ambient Air Quality Guideline for arsenic (5.5 ng m3 annual average) was substantially exceeded every year, with the highest concentrations measured during winter (maxima around 90 ng m3). The arsenic was strongly associated with the biomass combustion source and therefore the arsenic contamination was
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