Spatial and Temporal Variations of PM2.5 in North Carolina

Received: March 29, 2018
Revised: September 18, 2018
Accepted: November 16, 2018

Download Citation: RIS|BibTeX|https://doi.org/10.4209/aaqr.2018.03.0111  

Cite this article:

Cheng, B. and Wang-Li, L. (2019). Spatial and Temporal Variations of PM2.5 in North Carolina. Aerosol Air Qual. Res. 19: 698-710. https://doi.org/10.4209/aaqr.2018.03.0111

HIGHLIGHTS

• Higher PM_2.5 in central NC urban areas; significant reduction of PM_2.5 in 2005–2014.
• PM_2.5 were significantly higher in summer and lower in winter only in 2005–2011.
• Inorganic PM_2.5 were dominant part of PM_2.5 in 2005–2011 but not in 2012–2014.
• Inverse seasonal variation for NO₃^– and SO₄^2– mass fractions.
• This study provides insights to link the ambient PM_2.5 to various sources.

Keywords: Chemical compositions; Inorganic PM2.5; Spatiotemporal variations.

INTRODUCTION

By definition, PM_2.5 represents particulate matter (PM) with aerodynamic equivalent diameter ≤ 2.5 µm. As a criteria air pollutant in the United States (U.S.), it has gained intensive attention due to its adverse health effects, special role in visibility degradation, and the potential impact on climate (Boucher, 2000; Dominici et al., 2006; Haywood and Pope et al., 2009; Pui et al., 2014). Franklin et al. (2007) studied the relationship between PM_2.5 and mortality across the U.S. with a conclusion that elevated PM_2.5 mass concentration can lead to the increase in mortality; the responses to PM_2.5 exposures may differ for people of different ages and genders in different areas of the U.S. Dominici et al. (2006) estimated the health risk of PM_2.5 exposures and discovered that cardiovascular risks were increased by PM_2.5 short-term exposures with higher risk in the eastern U.S. Moreover, research has indicated that standalone PM_2.5 mass concentration is inadequate to vigorously explain the health effects of PM_2.5; detailed information of PM_2.5 chemical compositions is needed to establish a relationship between human health and PM_2.5 exposures (Franklin et al., 2008). In addition to spatially varied adverse effects on human health, a visibility research conducted by Malm et al. (1994) indicated that sulfate (SO₄^2–) and organic materials were more effective in reducing the visibility across the U.S. than the other components of PM_2.5. In the eastern U.S., SO₄^2– is the dominant component to contribute to light extinction. Similar investigations performed by Brewer and Adlhoch (2005) and Brewer and Moore (2009) suggested that ammonium sulfate ((NH₄)₂SO₄) and organic carbon matter (OCM) were the dominant contributors to light extinction in the southeastern U.S.; however, wildfires and windblown dust dominated in light extinction in the western U.S.

In ambient air, large spatiotemporal variations exist in mass concentration and chemical compositions of PM_2.5, caused by spatiotemporal variations of emission sources, formation and deposition processes, meteorological conditions, and atmospheric fate and transport (Martuzevicius et al., 2004; Seinfeld and Pandis, 2006; Bell et al., 2007; Wang-Li, 2015). The regional impacts of PM_2.5 may vary due to such spatiotemporal variations. Hu et al. (2014) used satellite-retrieved aerosol optical depth (AOD) data to estimate the spatiotemporal variations of PM_2.5 mass concentrations in the southeastern U.S. in 2001–2010; the results indicated that urban areas and major highways can display higher PM_2.5 concentrations than rural or mountain areas. In addition, around 20% reduction in PM_2.5 concentrations has been achieved over the past 10 years in the southeastern U.S. Previous research on the spatiotemporal variations of PM_2.5 mass and chemical compositions in the U.S. has indicated that PM_2.5 mass concentrations had higher values in the eastern U.S. and California and lower values in the central and northwestern regions of the nation. Moreover, PM_2.5 chemical components such as SO₄^2– and nitrate (NO₃^–) exhibited inverse seasonal variation patterns in eastern and western coast areas of U.S. (Bell et al., 2007). In addition, Hasheminassab et al. (2014) identified the main contributors to the PM_2.5 to be secondary inorganic PM_2.5 (iPM_2.5) in California. Another study performed in Cincinnati metropolitan area found that major components of PM_2.5 can be identified as organic carbon (OC) followed by SO₄^2–, elemental carbon (EC), crustal elements and trace metals (Martuzevicius et al., 2004). Similar research conducted by Saunders et al. (2015) in the northeastern U.S. identified two main PM_2.5 pollutants: OC and (NH₄)₂SO₄. Goetz et al. (2008) analyzed the major compositions of PM_2.5 in eastern NC from 2001 to 2004 at sites representing urban and rural sites; the results indicated that OC, SO₄^2–, ammonium (NH₄⁺), NO₃^– and EC were the major contributors to PM_2.5 mass; SO₄^2– and NH₄⁺ tended to be slightly higher in the rural site. Previous research has discovered that different areas exhibited different PM_2.5 chemical characteristics such that the regional impact of PM_2.5 may differ correspondingly.

The chemical compositions of PM_2.5 include ions (e.g., NH₄⁺, SO₄^2–, and NO₃^–), OC, EC, elements (e.g., crustal materials) and other unknown components (Malm et al., 1994; Frank, 2006). The iPM_2.5 is defined as the ion components, including NH₄⁺, SO₄^2–, and NO₃^–, in this paper. Different compositions of PM_2.5 can be linked to different emission sources and formation processes (Abdeen et al., 2014). As a subset of total PM_2.5, iPM_2.5 is formed through the partitioning of gas-phase NH₃ and particle-phase NH₄⁺. The formation of secondary iPM_2.5 can be characterized by the reactions between precursor gases such as ammonia (NH₃), nitric acid (HNO₃) and sulfuric acid (H₂SO₄). While H₂SO₄ and HNO₃ are mainly transformed from the primary pollutants, SO₂ and NO_x (NO_x = NO + NO₂), through photochemical reactions, NH₃ is directly emitted from emission sources such as agricultural operations (Seinfeld and Pandis, 2006). Because secondary iPM_2.5 constitutes a significant part of total PM_2.5 (Sawant et al., 2004; Katzman et al., 2010; Philip et al., 2014), it is important to establish a holistic understanding of the spatiotemporal variations of secondary iPM_2.5 such that the regional impact of PM_2.5 may be fully understood.

Aneja et al. (2003) investigated the relationship between NH₃ emissions from agricultural sources and ambient NH₄⁺ concentrations in the southeastern U.S. with spatiotemporally varied NH₃ emissions and NH₄⁺ concentrations data. This research indicated that higher NH₃ emissions led to higher NH₄⁺ at the given location. Other research discovered that in the rural areas of North Carolina (NC), ambient NH₃ concentration was high, sometime reaching up to 45.87 ppb (34.80 µg m^–3); ambient air was dominated by NH₃-rich conditions in those rural areas due to high NH₃ emissions from agricultural operations (Robarge et al., 2002; Walker et al., 2000b). Walker et al. (2004) compared the secondary iPM_2.5 concentrations at three sites located in NC Coastal Plain region with different NH₃ emission densities, and reported that NH₃ emitted from agricultural sources posed large influences on iPM_2.5 concentrations; higher total NH₃ emissions led to higher iPM_2.5 concentrations in rural areas. Furthermore, Walker et al. (2006) performed another study and reported a two-year average concentration of secondary iPM_2.5 from 1999 to 2000 to be 8.0 ± 5.84 µg m^–3 at an agricultural site in the southeastern area of NC; among the measured various iPM_2.5 chemical components, SO₄^2–, NO₃^–, and NH₄⁺ were the most dominant components.

As a complex mixture, various chemical components of PM_2.5 contribute to total PM_2.5 mass differently. The reduction of any specific chemical component may have different effects on the reduction of PM_2.5 mass. The in-depth understanding of spatial and temporal variations of PM_2.5 characteristics is the foundation to study regional impact of PM_2.5 and to develop effective control strategies of PM_2.5; more research is necessary to quantitatively characterize PM_2.5 mass and chemical compositions in spatial and temporal scales. This research was to quantitatively assess the variations of PM_2.5 and its associated chemical compositions in spatial and temporal scales in NC to advance our understanding of spatial and temporal variations of PM_2.5 in the region. 

METHODS

PM_2.5 Monitoring Stations in North Carolina

Investigation of PM_2.5 spatial and temporal variations started with identification of the EPA’s PM_2.5 monitoring stations in NC and obtaining PM_2.5 data for each given station. Under the State and Local Air Monitoring Stations (SLAMS) for criteria pollutants (EPA, 2016a), PM_2.5 mass concentrations were routinely monitored at 34 stations in NC from 2005 to 2014. In addition, under the EPA’s Chemical Speciation Network (CSN) (EPA, 2016b), PM_2.5 chemical speciation samples were taken and analyzed at 7 stations, where PM_2.5 mass measurements were concurrently taken. Fig. 1 shows the spatial distribution of these monitoring stations in the state. While the PM_2.5 mass concentration monitoring stations were evenly distributed across the whole state, most of the PM_2.5 chemical speciation monitoring stations were located in central areas of the state. Based on the topography of NC, the whole state can be divided into three geographic areas: mountains in the west, the Piedmont in the center, and the Coastal Plain in the east (NC SOS, 2018).

Fig. 1. PM_2.5 mass and chemical component measurement stations across North Carolina.

Under the SLAMS, PM_2.5 concentration measurements were taken once every day using either Federal Reference Method (FRM) or Federal Equivalent Method (FEM). Under the CSN, PM_2.5 chemical speciation samples were taken once every six days using the chemical speciation samplers that had three filter types with Teflon for mass concentration and elemental analyses, nylon for ion analyses, and quartz for OC and EC analyses.

Data Acquisition

To investigate the temporal and spatial variations, the 10 years (2005–2014) of PM_2.5 concentrations and chemical speciation data for all the monitoring sites in Fig. 1 were extracted from EPA’s AirData (EPA, 2016c). For analysis of seasonal variations, dataset was grouped into four seasons with spring in March, April and May; summer in June, July and August; fall in September, October and November; and winter in December, January, and February.

Data Adjustment

Organic carbon measurements reported in EPA’s AirData database should be adjusted to derive total mass of organic compounds. According to Turpin and Lim, (2001), Weber et al. (2003), Frank (2006), El-Zanan et al. (2009), and Dillner et al. (2012), the adjusted organic carbon matter (OCM) may be calculated using Eq. (1):

where OCM = organic carbon matter; OC_m = measured organic carbon; OC_b = field blank organic carbon. In this research, the adjusted OCM concentrations were used to check the mass closure of PM_2.5 chemical speciation.

In an effort to understand the contribution of each chemical component to the total PM_2.5 mass concentration, chemical speciation data were used to construct PM_2.5 chemical speciation distribution pie chart, also named the mass closure. In this PM_2.5 mass closure analysis, percentage of each chemical component was calculated using Eq. (2). For valid data selection, only those days having simultaneous measurements of PM_2.5 mass and chemical components were used to construct PM_2.5 mass closure. In addition, it was discovered in many cases, the sum of individual chemical component masses exceeded the measured PM_2.5 mass. This may be due to some artifacts such as adsorption of organics, inappropriate estimation of OCM, the loss of ammonium nitrate, water existing in the samples, and measurement errors (Andrews et al., 2000; Frank, 2006; Watson et al., 2008; Chow et al., 2015). To conduct mass closure analysis, when measured PM_2.5 mass concentration was less than the sum of individual chemical compositions masses, those data were excluded.

where P_i = percentage of the chemical component i to the total PM_2.5 mass concentration, C_i = measured or adjusted concentration of the chemical composition i, C_m = measured PM_2.5 mass concentration from Teflon filter.

In the period of 2004–2009, the PM_2.5 chemical speciation dataset included SO₄^2–, NO₃^–, NH₄⁺, Na⁺ and K⁺, OC/EC, and 48 elements, whereas for the years of 2010–2014 the PM_2.5 chemical speciation dataset included SO₄^2–, NO₃^–, NH₄⁺, Na⁺ and K⁺, OC/EC, and 33 elements. Since each element only accounted for very small portion of total PM_2.5 mass concentration, the individual elements were grouped into one category to construct the mass closure. In addition, since S has been taken into account in SO₄^2– measurements, and Na⁺ and K⁺ have been taken into account in Na and K elemental measurements, S element, Na⁺, and K⁺ were excluded to avoid double counting when analyzing the mass closure.

Statistical Analysis and Map Development

The relationship between PM_2.5 mass concentration and PM_2.5 chemical compositions were characterized by Spearman correlation coefficient (R). In addition, Tukey test and Mann-Kendall trend test were performed to characterize the general change trend of the dataset in spatial and temporal scales under 0.05 significance level. The dataset in this research may not follow normal distribution; therefore, parametric test such as Tukey test may lack power to interpret the change trend of the data. The distribution of the data was examined as well. All the statistical tests were conducted using R software. To visually illustrate spatial variations, maps reporting PM_2.5 monitoring stations and associated PM_2.5 concentrations over time were developed using ArcMap 10.4.

RESULTS AND DISCUSSION

Spatial and Temporal Variations of PM_2.5 Concentration in North Carolina

The PM_2.5 concentrations measured by the FRM samplers from 2005 to 2014 were used to detect the spatiotemporal variations. For illustration purpose, Fig. 2 shows the annual average PM_2.5 concentrations at each site for 2005, 2008, 2012 and 2014. Based on continued research and updated scientific evidence on the adverse effects of PM_2.5, the PM_2.5 National Ambient Air Quality Standards (NAAQS) have been revised two times in the past 10 years. The PM_2.5 24-hour average concentration was strengthened from 65 µg m^–3 to 35 µg m^–3 in 2006; in addition, the primary PM_2.5 annual average concentration threshold was strengthened from 15 µg m^–3 to 12 µg m^–3 in 2012. Selection of these years was to reflect the PM_2.5 regulation changes through the time (EPA, 2018).

Fig. 2. Spatial and temporal variations of annual average PM_2.5 concentrations (µg m^–3) in NC.

In general, the annual average PM_2.5 concentrations were higher in central urban NC, the Piedmont areas, while the other areas exhibited lower annual average PM_2.5 concentrations. Specifically, large cities in central NC such as Charlotte and Lexington exhibited higher annual average PM_2.5 concentrations for the past 10 years, while lower PM_2.5 concentrations always occurred in western mountain areas and eastern Coastal Plain areas. The observation of such spatial variations in annual average PM_2.5 concentrations in NC was consistent with the finding of Hu et al. (2014). The spatial pattern of PM_2.5 concentrations may be attributed to the spatial heterogeneity of emission sources because major emission sources of primary PM_2.5 and precursor gases of secondary iPM_2.5 such as NO_x and SO₂ were mainly located in urban areas. More specifically, airport operations, foundries, food processing plants and steel mills were located in Charlotte (see Fig. 1), while glass plants and furniture manufacturing plants were located in Lexington. Among these emission sources, airport operations, steel mills, and glass plants can emit primary PM_2.5, NO_x and SO₂ at the same time (NEI, 2008). In addition, the topography might also affect atmospheric airflow dynamics and transport of PM_2.5. As reported by Chow et al. (2006), high elevation may prevent the transport of PM_2.5 toward mountain areas. The air mass blowing from coastal areas is cleaner than the inland. Various air pollutions may be picked up during the movement of air mass toward Piedmont area (central NC). If the air mass was trapped and circulated within the central NC, this may result in higher PM_2.5 mass concentrations. Whichever direction the wind may come from, the monitoring sites in the mountain areas may not be exposed to the air mass laden with higher PM_2.5 concentrations.

As for temporal variation, Mann-Kendall trend test indicates that the annual average PM_2.5 concentrations were significantly decreased from 2005 to 2014 at all the monitoring sites; annual average reduction of PM_2.5 concentrations ranged from 0.51 µg m^–3 in the city of Raleigh to 0.86 µg m^–3 in the city of Hickory. In 2005, there were 9 sites having annual average PM_2.5 mass concentration greater than 15 µg m^–3, the annual concentration threshold under the NAAQS back then; on the other hand, in 2014, all the sites had the annual concentrations below the latest NAAQS threshold of 12 µg m^–3. The temporal reduction of PM_2.5 mass concentration from 2005 to 2014 was reflective of the implementation of NAAQS. Under the Clean Air Act (CAA) and Clean Air Act Amendment (CAAA), each state has to develop effective reduction plans of six criteria pollutants known as the SIPs for nonattainment areas to meet NAAQS (EPA, 2016d). In 2002, NC Clean Smokestacks Act was passed to regulate the NO_x and SO₂ emissions from coal-burning power plant facilities and set the reduction plan of 77% in NO_x by 2009 and 73% in SO₂ by 2013. The utilization of control devices such as scrubbers and low-NO_x burners have guaranteed the compliance of the act (NCDAQ, 2014). The annual average PM_2.5 concentrations were significantly decreased to be below 14 µg m^–3 in 2008 and 11 µg m^–3 in 2009 across the whole state. The significant reduction of annual average PM_2.5 concentrations was also reflective of EPA’s Clean Air Interstate Rule (CAIR), which was implemented in 2009. As requested by the rule, NC had to control both ozone and PM_2.5 pollutants, and electric generating units (EGUs) were required to control both NO_x and SO₂ emissions (EPA, 2016e). Moreover, as the replacement of CAIR, the implementation of Cross-State Air Pollution Rule (CSAPR) in 2012 further guaranteed the reduction of PM_2.5 concentrations, measures such as the lower S coal and improved pollution control equipment in power plants helped reduce the emissions of both SO₂ and NO_x (EPA, 2016f). Consequently, the annual average PM_2.5 concentrations became below 10 µg m^–3 in 2014 across the state.

To further investigate the reduction trend of PM_2.5 concentrations in NC, higher temporal resolution datasets (monthly average PM_2.5 concentrations) were analyzed. Results at 4 representative sites are shown in Fig. 3.

Fig. 3. Temporal variations of monthly average PM_2.5 concentrations at 4 representative sites.

The monthly average PM_2.5 concentrations at these 7 sites exhibited reduction trend from 2005 to 2014 as well. In addition, a seasonal variation of PM_2.5 concentrations can also be observed from the above plots. Furthermore, the 10 years can be divided into 3 consecutive periods based upon the reduction trend of PM_2.5 concentrations: 2005–2007, 2008–2011, and 2012–2014. The seasonal variations of PM_2.5 mass concentrations in these 3 periods are summarized in Table 1.

As can be seen from Table 1, in general, PM_2.5 concentrations were significantly higher in summer and lower in winter at 7 monitoring sites; however, the Tukey test indicated that this seasonal trend was only significant in 2005–2007 and 2008–2011, while no significant seasonal variation can be observed in 2012–2014 at 6 out of 7 sites. The seasonal variations were only significant at sites in the city of Winston-Salem in 2012–2014 period. This may be due to the aforementioned implementation of CSAPR in 2012 and effective emission control on NO_x and SO₂ continued in 2012 in NC. Thus, NO_x and SO₂ were less available in all four seasons compared with the emission conditions before 2012; less seasonal variations of various chemical components of PM_2.5 combined with opposite seasonal variations of OCM and SO₄^2– salts led to insignificant variations of PM_2.5 in 2012–2014 

Spatial and Temporal Variations of the iPM_2.5 Chemical Compositions

The variations of PM_2.5 concentrations may result from the variations in various PM_2.5 chemical compositions; therefore, spatiotemporal variations of PM_2.5 chemical compositions were analyzed and compared with the PM_2.5 concentration change trend at each location. Fig. 4 shows the analyses results of major iPM_2.5 composition changes over time at 7 urban sites located in the cities of Asheville, Hickory, Lexington, Winston-Salem, Rockwell, Charlotte, and Raleigh (see the map in Fig. 1). All the PM_2.5 mass concentrations were measured by the non-FRM samplers.

Fig. 4. Temporal variations of annual average concentrations of PM_2.5 and iPM_2.5 chemical compositions at 7 urban sites (iPM_2.5 = NH₄⁺ + SO₄^2– + NO₃^–).

In general, there is no significant difference among 7 PM_2.5 chemical speciation monitoring sites for each of the 3 major iPM_2.5 chemical components, SO₄^2–, NH₄⁺ and NO₃^–. The exceptions were that SO₄^2– concentrations in Lexington were significantly higher than in Raleigh in 2006, and NH₄⁺ concentrations in Lexington were significantly higher than in Asheville in 2008 and 2009. Asheville was located in western mountain areas while Raleigh was located on the border of Piedmont and Coastal Plain areas (see Fig. 1); therefore, the impact of topography may explain the spatial variations of SO₄^2– and NH₄⁺ in these years. Similar spatial distribution of the three major iPM_2.5 chemical compositions at 7 urban sites may indicate the similarity of the atmospheric chemistry background at the 7 monitoring sites, as all of them are located in urban areas. In rural areas, large NH₃ emissions from agricultural operations have resulted in NH₃-rich conditions in ambient air (Saylor et al., 2010; Walker et al., 2000a, b); therefore, atmospheric chemistry background in rural areas was different from those in urban areas. Some spatial variations of iPM_2.5 chemical compositions may be expected between urban and rural areas; further research is needed to establish holistic understanding of the spatial variations in the future.

Significant reduction trend in SO₄^2–, NO₃^–, and NH₄⁺ concentrations from 2005 to 2014 occurred at all 7 monitoring sites, which is consistent with the reduction trend of total PM_2.5 concentrations. More specifically, the annual average PM_2.5 level at the largest city in NC, Charlotte, decreased from 18.21 ± 8.10 µg m^–3 in 2005 to 9.12 ± 3.59 µg m^–3 in 2014 and the SO₄^2–, NH₄⁺ and NO₃^– concentrations also decreased significantly at this location from 6.42 ± 3.71 µg m^–3 to 1.71 ± 0.76 µg m^–3, from 2.16 ± 1.18 µg m^–3 to 0.50 ± 0.34 µg m^–3, and from 0.88 ± 0.96 µg m^–3 to 0.62 ± 0.75 µg m^–3, respectively. Quantitatively, at Charlotte site, a 9.09 µg m^–3 annual average PM_2.5 mass concentration reduction was achieved from 2005 to 2014; at the same time, 4.71 µg m^–3, 1.66 µg m^–3, and 0.26 µg m^–3 annual average concentration reduction were achieved for SO₄^2–, NH₄⁺, and NO₃^–, respectively. Together, iPM_2.5 contributed to around 73% of the PM_2.5 mass concentration reduction. The similar reduction trend of iPM_2.5 was observed at the other monitoring sites as well. Annual average OCM concentration was reduced from 5.63 ± 2.57 µg m^–3 in 2005 to 3.63 ± 1.89 µg m^–3 in 2014 at Charlotte site. The OCM contributed to 22% of the reduction in PM_2.5 mass concentration. The exceptions were Asheville, Hickory, and Rockwell; the OCM concentrations did not exhibit a significant reduction trend over the past 10 years. Concurrent variations of PM_2.5 mass and iPM_2.5 chemical compositions provided evidence of important contribution of iPM_2.5 to the reduction of total PM_2.5 mass in NC.

Trends in Extrema of PM_2.5 and its Chemical Component Concentrations

The trends in extrema of PM_2.5 mass and its chemical component concentrations at one of the seven sites (Charlotte) are shown in Fig. 5. As can be seen from Fig. 5, the temporal variation in the distribution of PM_2.5 mass and its chemical component concentrations occurred in the three consecutive periods. Fewer high concentrations can be observed in the period of 2012 to 2014 in Charlotte. The 98^th percentile of PM_2.5 mass concentration in 2012–2014 was about 18.6 µg m^–3, which was less than the primary and secondary 24-hr PM_2.5 NAAQS (35 µg m^–3). The temporal changes in various chemical components caused the temporal change in the distribution of PM_2.5 mass concentrations. The reduction of high PM_2.5 concentrations was the important evidence of air quality improvement in North Carolina over the past 10 years.

Fig. 5. Statistical distributions of daily concentrations of PM_2.5, SO₄^2–, NH₄⁺, NO₃^–, OCM, EC at Charlotte site.

Correlations between PM_2.5 and Secondary iPM_2.5 Component Concentrations

To better understand the contribution of iPM_2.5 components to the total PM_2.5 mass, the correlations between PM_2.5 total mass and iPM_2.5 chemical components were analyzed and are shown in Table 2. The analyses include yearly and seasonal correlations to account for the impact of ambient conditions in four seasons.

As can be seen from Table 2, at 7 monitoring sites of NC, NH₄⁺ and SO₄^2– were strongly correlated with PM_2.5 mass concentration (R > 0.58); however, the correlation between NO₃^– and PM_2.5 mass concentration varied in 4 seasons with stronger correlation in winter than in summer, spring and fall. The observation was consistent with the research performed by Bell et al. (2007), in which NH₄⁺, SO₄^2–, and NO₃^– were determined to be 3 significant components of PM_2.5, exhibiting stronger correlation with total PM_2.5 mass. Stronger correlation of PM_2.5 mass concentration with NH₄⁺ and SO₄^2– indicated the day-to-day covariation, while the fluctuated correlation between PM_2.5 and NO₃^– can be attributed to the semi-volatile characteristic of NH₄NO₃ (Olszyna et al., 2005).

Evaluation of PM_2.5 Mass Closure

Analysis of the mass fraction of each chemical component may help to understand the individual chemical components’ contributions to the total PM_2.5 concentrations. Fig. 6 shows the resultant mass distribution charts of the analysis at 4 selected monitoring locations, separated by the three periods.

Fig. 6. Mass distributions of PM_2.5 chemical compositions at 4 representative monitoring sites.

The OCM was the dominant component of PM_2.5 in the 3 periods at 7 monitoring sites, especially in 2012–2014 accounting for 34.4–37.0% of total PM_2.5. Of the total PM_2.5 at seven monitoring sites, the three major iPM_2.5 components together accounted for 34.1–45.9% from 2005 to 2007, 34.8–39.1% from 2008 to 2011, and 28.3–30.9% from 2012 to 2014. Among the three major iPM_2.5 chemical components, SO₄^2– was the dominant contributor to the total PM_2.5 mass. The mass fraction of SO₄^2– exhibited a significant reduction trend in the 3 periods. Contribution of NH₄⁺ to total PM_2.5 mass was also significantly reduced, while no significant reduction trend can be observed for the mass fraction of NO₃^–. The contribution of unidentified components (“other”) to total PM_2.5 mass was actually increased throughout the whole periods. The increase of unidentified component mass fraction may be caused by the inappropriate estimation of OCM through the OCM/OC ratio of 1.4; this conversion ratio may be higher in the later period (Blanchard et al., 2013). The relative contributions of various chemical components to the total PM_2.5 mass resulted from different emission sources and atmospheric physiochemical processes. As mentioned above, in the past 10 years, several regulations such as CAIR and CSAPR have been promulgated to reduce the emissions of SO₂ and NO_x in eastern U.S. to control the pollution of PM_2.5; the reduction of the mass fraction of the secondary iPM_2.5 proved the effectiveness of all these regulation and control policies.

In addition, seasonal variation analysis indicates that NO₃^– contribution to the total PM_2.5 had a significant seasonal pattern with higher fraction in winter and lower fraction in summer, while the mass fraction of SO₄^2– exhibited an inverse seasonal pattern with higher fraction in summer and lower fraction in winter. No significant seasonal variations can be observed for the mass fraction of NH₄⁺. This can be justified by the semi-volatile characteristic of NH₄NO₃. Low temperature (T) and high relative humidity (RH) in winter favored the formation of NH₄NO₃ (Olszyna et al., 2005). However, in contrast to NO₃^– salts, SO₄^2– salts had very low vapor pressure; therefore, most of the (NH₄)₂SO₄ stayed in particle phase even in summer. H₂SO₄ was transformed from SO₂ through photochemical reactions; more intense solar radiation in summer facilitated the formation of H₂SO₄ leading to more formation of (NH₄)₂SO₄ (Seinfeld and Pandis, 2006).

No significant spatial variations of 3 major iPM_2.5 chemical components were observed at the 7 monitoring sites. This may be due to similar chemistry background of atmosphere at these 7 sites. However, high NH₃ emissions from agricultural sources in rural areas may pose greater influence on the chemical compositions of PM_2.5; the relative contributions of iPM_2.5 to PM_2.5 mass may vary correspondingly. The monitoring data in rural areas are scarce in NC; future research is needed to fill this data gap so that analysis may be conducted for agricultural areas, where the highest base precursor gas (NH₃) concentrations occurred. At the 7 sites in NC, the 3 major iPM_2.5 components accounted for the largest portion of PM_2.5 mass in 2005–2011. However, in 2012–2014, OCM accounted for the largest portion of PM_2.5 mass. The reduction trend of PM_2.5 chemical components was consistent with finding of the research performed by Saylor et al. (2015) in other locations in southeastern U.S.

CONCLUSIONS

The variations of PM_2.5 mass concentrations in North Carolina from 2005 to 2014, measured at the EPA’s monitoring sites, were found to be statistically significant on spatial and temporal scales, with the highest values recorded in the urban areas of central NC. A significantly decreasing trend in PM_2.5 levels was also observed across the state during this period. Temporal variations in the iPM_2.5 were consistent with this trend, exhibiting a concurrent reduction in concentration and thereby indicating the critical contribution of secondary iPM_2.5 to the total PM_2.5 mass. Although the PM_2.5 concentrations displayed a significant seasonal variation, with higher concentrations in summer and lower concentrations in winter, respectively, between 2005 and 2011, this variation was insignificant between 2012 and 2014 at 6 of the 7 chemical speciation monitoring sites. Furthermore, iPM_2.5 composed the largest mass fraction of the PM_2.5 for 2005–2011, but organic carbon matter (OCM) was the dominant component for 2012–2014. The iPM_2.5 mass fractions also displayed significant seasonal variations, with NO₃^– and SO₄^2– exhibiting inverse variations. The results of this study improve our holistic understanding of PM_2.5 and iPM_2.5 on a regional scale.