Association between airborne PM_2.5 chemical constituents and birth weight—implication of buffer exposure assignment

Research generally indicates associations between gestational exposure to air pollution and adverse birth outcomes, although findings are inconsistent. For instance, particulate matter with aerodynamic diameter ≦̸2.5 μm (PM_2.5) was associated with birth outcomes in California, but not in Oslo, Norway [1–3]. Although results vary, several reviews concluded that weak to moderate associations exist between PM_2.5 and birth outcomes [4–6].

One potential explanation for inconsistent results is substantial variation in PM_2.5's chemical structure by location, which may affect toxicity. For instance, PM_2.5 sulfate (SO₄) levels are higher in the eastern US, with higher PM_2.5 nitrate (NO₃) levels in the west [7]. A limited number of studies explored PM_2.5 chemical constituents with birth outcomes. Elemental carbon (EC) was associated with lower birth weight in Atlanta [8]. In the New England region of the US, aluminum (Al), EC, nickel (Ni), potassium (K), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) were linked to lower birth weight [9–11]. In California, lower birth weight was associated with ammonium ion (NH₄⁺), bromine, copper, EC, iron, manganese, SO₄, sulfur, Ti, V, and Zn [12]. The US Environmental Protection Agency (EPA) identified study of health associations of particle chemical constituents as a critical research need [13].

Another possible explanation is the variation in exposure methods. Typically, air pollution exposure is estimated by: (1) assigning measurements from the nearest monitor within a certain distance (i.e. buffer) from residence [14], (2) averaging or interpolating values from monitor(s) within geographic unit(s) (e.g. county) [15], or (3) combining alternative sources (e.g. traffic, satellite) and/or monitor data to predict concentrations at a given point or area [16]. These methods were applied in many studies to estimate exposure based on residence (e.g. mother's actual address or residential county at birth) [17–21]. Applying large buffers (or spatial units) can introduce exposure misclassification due to spatial heterogeneity of pollution [22]. However, applying small buffers (or spatial units) can limit sample size and lower statistical power. Furthermore, EPA monitors tend to be located in urban environments [12]. Because people with low socio-economic status (SES) are more likely to live in urban environments in the US, small buffers might exclude sub-urban and rural persons, resulting in different populations than would be selected with larger buffers. In fact, literature reviews identify exposure assignment as a key challenge in air pollution and birth outcomes research [4–6].

Buffer sizes for air pollution can vary from a few to 50 km [23, 24]. Researchers generally select these distances based on previous studies or assumptions of reasonable values, but typically do not systematically address variation in spatial heterogeneity by pollutant [8, 25]. This raises questions regarding how well estimated exposures represent actual exposures and creates challenges for comparisons across studies.

A few studies compared results under various exposure assignments with inconsistent results. Third trimester particulate matter with aerodynamic diameter ≦̸10 μm (PM₁₀) exposure was associated with low birth weight (LBW: birth weight <2500 g) for mothers living ≦̸1 mi (1.6 km) from monitors, with no associations using buffers >1 mi in Los Angeles [26]. In contrast, PM₁₀ and PM_2.5 associations for birth weight were comparable under buffers of 5, 10, and 20 km, and county-based assignment in North Carolina [27]. Questions remain regarding the implications of buffer size given pollutants' different spatial heterogeneity. In particular, few studies have explored the impacts of choice of buffer size on health associations for PM_2.5 chemical constituents. Patterns of surface concentrations can differ by PM_2.5 constituents, with some showing relative spatial homogeneity at long distances, while others exhibit spatial heterogeneity within short distances [22].

We investigated associations between PM_2.5 and PM_2.5 chemical constituents and birth weight in Connecticut, in the New England region of the US. We address limitations of previous studies [9–11, 28] by investigating: (1) whether effects differ by chemical constituents, in addition to estimating associations for PM_2.5 total mass; and (2) implications of different buffer sizes to assign exposure. Previously, exposure was estimated based on mother's county of residence, whereas here we benefited from detailed birth certificates that include exact mothers' residence. Furthermore, we analyzed whether associations differ by neighborhood SES (e.g. poverty level). Luo et al found that adverse birth outcomes were associated with lower SES neighborhoods in addition to lower individual SES [29]. Our previous studies could not control for neighborhood-level SES due to lack of exact residence. To date, no previous studies evaluated choice of PM_2.5 constituents' buffer size on health associations in the Northeastern US, which is more densely urbanized and has different pollutant composition than other US regions.

Birth data

Air pollution and weather data

Exposure estimation

Statistical analysis

Population characteristics and exposures

Associations between PM_2.5 total mass and chemical constituents and birth weight

Using a 30 km buffer, PM_2.5 and some chemical constituents (Ca, Cl, EC, Ni, NO₃, Na⁺, SO₄, Ti, V and Zn) were associated with lower birth weight. For instance, birth weight decreased 9.6 g (95% confidence interval (CI): 7.0, 12.2) and 16.8 g (95% CI: 9.1, 24.5) per interquartile range (IQR) increase of PM_2.5 total mass and PM_2.5 EC, respectively. Higher levels of Si were associated with higher birth weight. Other PM_2.5 chemical constituents did not show significant associations with birth weight.

We examined how effect estimates were affected by the choice of buffer size for exposure assignment (figure 1). For comparability, results from different buffer sizes are presented using the same increment of exposure, the IQR of the 30 km buffer (table 1). PM_2.5 total mass was associated with lower birth weight under all exposure methods with similar point estimates and confidence intervals. For example, an IQR increase in PM_2.5 was associated with lower birth weight of 11.6 g (95% CI: 7.9, 15.3), 10.4 g (95% CI: 7.6, 13.2) and 10.1 g (95% CI: 7.9, 12.3) with 10 km, 20 km buffer, and county-based assignment, respectively.

Zoom In Zoom Out Reset image size

On the other hand, results across buffer sizes were less consistent for most PM_2.5 chemical constituents. CIs were generally wider and significant associations were not observed with smaller buffer sizes. County-based exposure assessments were similar to those of larger buffers (20–30 km) (figure 1). For instance, an IQR increase in PM_2.5 NO₃ was associated with 9.2 g (95% CI: -16.4, 34.7) higher birth weight using a 10 km buffer, but 13.2 g (95% CI: 8.0, 18.4) and 11.3 g (95%CI: 5.1, 17.5) lower birth weight using a 30 km buffer, and county-based exposure, respectively. For PM_2.5 Al, NH₄⁺, and OCM, associations were null across all buffer sizes.

Results of logistic regression for LBW are shown in figure 2. Patterns of results across buffer sizes were similar to those of the linear regressions. PM_2.5 total mass associations were consistent across all exposure methods, and PM_2.5 chemical constituents showed narrower CIs as buffer size increased. While patterns were similar, results for several constituents did not reach statistical significance with larger buffer sizes (e.g. V or Zn).

Zoom In Zoom Out Reset image size

Results were almost the same using the closest monitor rather than distance weighted average if multiple monitors were available, since monitoring networks of PM_2.5 chemical constituents are sparse, resulting in little difference between two assignment methods (supplemental figure 1). Effects of PM_2.5 total mass for exposure assigned by the closest monitor are slightly weaker than results using the inverse distance weighted average (supplemental figure 3).

As sensitivity analyses, we implemented two-pollutant linear regression models and trimester exposure models for chemical constituents that showed associations with a 30 km buffer. Supplemental figure 4 showed results of co-pollutant models. For trimester models, pollutants which showed consistent associations in all models were shown (supplemental table 4). Al, Ca, SO₄, and V showed associations only at first trimester. Associations were observed in multiple trimesters for PM_2.5 total mass and Cl. Si showed lower health risk with higher exposures in the third trimester.

Population characteristics in relation to distance from monitors

We found that PM_2.5 total mass is associated with lower birth weight, which is generally consistent with previous research. Similar associations were found in studies conducted in California, eastern US and Europe [11, 39, 40]. A few studies did not find this association [3, 41], which could relate to various chemical structures across the regions [7].

Our findings indicate that effect estimates differ by chemical constituents, implying that the toxicity of PM_2.5 might be affected by its chemical constituents. For example, exposure to PM_2.5 Ca, Cl, EC, Ni, NO₃, Na⁺, SO₄, Ti, V and Zn are associated with lower birth weight with a 30 km buffer, while others constituents are not. These results, however, should be interpreted cautiously; these constituents lost statistically significant associations with other buffers, particularly smaller buffers, and some other constituents exhibited associations at other buffer sizes (figure 1). These findings indicate that different exposure assignment might play a role in the inconsistency of previous studies' results.

Exposure misclassification could contribute to the inconsistency. Some pollutant concentrations can decay over short distances, while others retain the same level for long distances. Supplemental figure 5 shows scatter plots of correlations of monitor pairs and distance between monitors for pollutants within the Northeastern US. For instance, PM_2.5 total mass and NO₃ show higher homogeneity, while PM_2.5 Al and Cl show high heterogeneity within short distances, implying exposure misclassification is more likely for PM_2.5 Al and Cl. PM_2.5 Al did not exhibit associations with birth weight for any buffer size, which may relate to exposure misclassification even with smaller buffers. Since subject numbers were substantially limited within 5 km from monitors compared to 30 km (table 1), we have limited ability to investigate this issue. Furthermore, exposure to PM_2.5 NO₃ or Cl was associated with lower birth weight with larger buffers, but smaller buffers produced null results (figure 1). Although association patterns across buffer sizes for NO₃ or Cl are similar, Cl exposure estimates likely include higher exposure misclassification than NO₃, due to spatial heterogeneity. Our findings suggest that the implications for exposure misclassification due to choice of buffer size differ by pollutant.

Many studies explored PM_2.5 total mass and birth weight. For instance, associations are found in Atlanta with 4 mi (6.4 km) buffer and in California with 5 km buffer [1, 8]. Similarly, association between PM_2.5 total mass and birth weight is reported in Connecticut with county-based assignment, where the average county area equals that of a circle with 22.6 km radius [28]. These findings are consistent with our results. Limited studies are available for PM_2.5 chemical constituents, and identified constituents vary by study. PM_2.5 EC and Zn were associated with lower birth weight with 30 km buffer in the current study, which is consistent with a previous study in Connecticut with county-based assignment and a study in California with 20 km buffer [11, 12]. A study in Atlanta found a PM_2.5 EC effect with 4 mi buffer [8], while we did not observe associations with small buffers (5–15 km). Likewise, PM_2.5 Al NH₄⁺, and Si were associated with birth weight in other research [8, 11, 12], but we did not find associations for Al and NH₄⁺ with any buffers, and found inverse effects for Si with larger buffers. In addition to various exposure assignments among studies, inconsistent results may be explained by correlations among constituents, which differ by region due to local emissions. High correlations are inevitable for PM_2.5 constituents, and making interpretation of multi-pollutant models difficult. Adapting source appointment methods could be one way to address this issue [42].

In addition to exposure misclassification from spatial heterogeneity of pollutants, the choice of buffer size has other implications such as sample size. Sample sizes are limited for some pollutants with small buffer sizes, which likely contribute to large confidence intervals (figure 1). Due to fewer monitors, sample sizes are particularly small for PM_2.5 chemical constituents with 5 or 10 km buffers, making interpretation of results challenging even though these buffer sizes have less exposure misclassification. Furthermore, areas close to monitors reflect more minority and low SES populations (table 2). Although we and other researchers adjust for demographic and SES variables, the skewed distribution of population characteristics may affect results and comparisons across studies. Figure 3 illustrates that choosing an exposure method involves the tradeoffs of the impacts of exposure misclassification, sample size, and population characteristics. Researchers should take account for those factors when their studies rely on ambient monitors.

Zoom In Zoom Out Reset image size

A key strength of this paper is the use of state-wide detailed birth certificate data for seven years with actual residential location. With this data, we have a long enough time series to avoid cohort bias [43]. In addition, data on the specific residence of each mother enable us to use multiple buffers to assign individual-level exposure and to explore the implications of exposure methods in relation to associations, exposure misclassification, sample size, and population characteristics. Our study adds evidence on associations for PM_2.5 and several PM_2.5 chemical constituents and birth weight. Our results indicate that some PM_2.5 chemical constituents seem more harmful than others, which may indicate that regulating PM_2.5 chemical constituents could be more efficient than current regulations based on PM_2.5 total mass. Regulating specific PM_2.5 chemical constituents might be challenging, because many constituents are highly correlated (supplemental table 1), making difficult to identify single toxic chemical constituent. We tried co-pollutant model for low-correlated constituents, but analysis in more than three pollutants model leads to difficult interpretation. Furthermore, these chemical constituents are likely come from multiple emission sources, which is challenging for regulation [9].

How pollutants affect birth weight is not fully understood from a biological viewpoint, although multiple mechanisms likely occur simultaneously. One potential explanation is that pollutants directly affect fetal growth [14]. Another hypothesis is that some pollutants react with oxygen, affecting blood flow and restraining oxygen and nutrition transfer from mother to fetus [44]. Identification of which constituents and types of particles are most harmful may help inform studies on physiological mechanism.

There are several limitations in this study. First, our exposure estimates rely on ambient monitors. This approach mirrors commonly applied methods in epidemiological studies, but monitor locations tend to be in urban and low SES areas in the US [12, 38]. Therefore exposure values obtained from ambient monitors may not reflect exposure levels of the general population. Additional monitors are needed for sub-urban and rural area, especially for PM_2.5 chemical constituents. Utilizing land-use regression, air quality modeling, or satellite imagery to predict concentrations in locations without monitors are emerging approaches. The spatial resolution of satellite methods can reach 1 × 1 km, implying less spatial exposure misclassification [16, 45]. However, these methods have some uncertainties [46], and still require some monitor-based data for inputs and model evaluation [24, 47]. Several papers compared effect estimates based on alternative exposure methods [3, 17–19], but none of them compared effects of PM_2.5 chemical constituents. In the future, we could compare effect estimates of chemical constituents based on alternative exposure methods. Another limitation is that we do not have personal time-activity patterns during gestation, describing the locations of mothers (e.g. home, work), which could lead to potential exposure misclassification. Cost and ethics, particularly for vulnerable pregnant women, hinder frequent use of personal monitors over long time periods [48].

In this paper, we explored gestational exposure to PM_2.5 and its chemical constituents and birth weight, and the impacts of various buffer sizes to assign exposure. Some PM_2.5 chemical constituents appear more harmful than others, and further studies are warranted. To understand toxicity of some PM_2.5 chemical constituents, more studies focusing on the relationship between PM_2.5 chemical constituents and health are warranted. Improvement of monitoring networks or alternative methods of estimating exposure is desired, since the current situation (e.g. limited monitors, infrequent observation, etc) hinder detailed analysis in epidemiological studies. Homogeneous pollutant levels within a certain distance or geographic unit is a common assumption in many environmental epidemiology studies. Our findings, however, suggest that the validity of this assumption varies by pollutant, and that the choice of exposure method impacts exposure misclassification, sample size, population characteristics, and eventually health associations. This finding should not be interpreted to suggest a particular buffer size, but rather we encourage researchers to consider the related tradeoffs of sample size, exposure misclassification, and demographic characteristics that may affect generalizability to areas outside the buffer. Further studies are warranted in different locations, since the degree of heterogeneity of pollutant levels, monitoring networks, pollutant sources and population characteristics differ by region.

This study was funded by the US Environmental Protection Agency (EPA RD 83479801) and the National Institutes of Health (NIEHS R01ES019560, R01ES016317, R01ES019587). The authors thank Yale Center for Perinatal, Pediatric and Environmental Epidemiology. The authors also appreciate the Connecticut Department of Public Health, Human Investigation Committee, which provided data.

none declared.