Zinc nitrate

We estimated population-level exposures for different groups (e.g., race/ethnicity) to PM_2.5 and for the following 14 PM_2.5 components measured by the U.S. EPA’s national monitoring network: sulfate (SO₄^2–), nitrate (NO₃^–), ammonium (NH₄⁺), organic carbon matter (OCM), elemental carbon (EC), sodium ion (Na⁺), aluminum (Al), calcium (Ca), chlorine (Cl), nickel (Ni), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn). These components were selected because they contribute ≥ 1% to total PM_2.5 mass for yearly or seasonal averages, and/or have been associated with adverse health outcomes in previous studies including mortality, heart rate, heart rate variability, and low birth weight (Bell et al. 2007 (link), 2009 (link); Dominici et al. 2007 (link); Franklin et al. 2008 (link); Huang et al. 2012 (link); Lippmann et al. 2006 (link); Ostro et al. 2007 (link), 2008 (link); Rohr et al. 2011 (link); Wilhelm et al. 2012 (link)).
Daily air pollution measures were obtained for 2000 through 2006 (U.S. EPA 2011a ). Pollutant monitors were matched to U.S. census tracts, which are geographic units representing small subdivisions of a county and are the smallest spatial unit for which demographic variables of interest were available. Tracts from the 2000 Census (U.S. Census Bureau 2007 ) were designed to have an optimal population of 4,000 persons (range, 1,500–8,000) and to follow government boundaries (e.g., county), geographic features (e.g., rivers), or other identifiable features (e.g., roadways), where possible. The median land area of the 2000 census tracts in the continental United States was 5.06 km².
Census tracts in the continental United States were included in our analysis if they had PM_2.5 component monitors in operation for ≥ 3 years with ≥ 180 days of observations during the study period. Results were based on 219 monitors in 215 census tracts. Land use near monitors was 43% residential, 34% commercial, 8% industrial, 8% agricultural, and 4% forest.
We calculated long-term averages for each pollutant and 2000 census tract with a monitor for that pollutant. If multiple monitors were present for the same pollutant in a single tract, we averaged daily monitor values within a tract, and then averaged daily values to generate long-term averages. The population and area of census tracts varied. The mean (± SD) distance between a census tract’s centroid and monitor was 2.3 km ± 4.9 km (median 0.8 km; maximum 46.7 km).
For each census tract, we considered population characteristics (U.S. Census 2007 ):
We excluded census tracts with populations ≤ 100 (n = 1; for tract with population = 1). For each population characteristic and category (e.g., race/ethnicity, Hispanic), we estimated the average exposure to each pollutant for that group in the United States as a whole by weighting levels in each census tract by the population as
where Y_i^k is the national average estimated exposure to pollutant k for persons with characteristic i (e.g., Hispanic), j is the number of census tracts with pollutant data (J = 215), P_i,j is the number of persons with characteristic i in census tract j, and x_j^k is the concentration of pollutant k for census tract j. This provides an estimate of average exposure for each pollutant and population group, accounting for population size and pollutant levels in each census tract. In addition, we performed univariate regression to estimate differences in exposure to PM_2.5 and for each component according to census tract characteristics (e.g., percentage of persons unemployed), which are expressed as the percent change in exposure compared with overall mean levels associated with a 10% increase in a given population characteristic.
Whereas the regression analysis investigated whether some groups had higher exposures than others among areas with monitors, we further contrasted population characteristics between census tracts with and without monitors for PM_2.5 or its components. We calculated population characteristics for census tracts with and without monitors and performed univariate logistic regression to estimate the percent increase in the probability of a census tract having a monitor with a 10% increase in each population characteristic. This analysis investigated whether some populations are better covered by the existing monitoring network than others.

Birth data. Birth certificate data for Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, Washington, DC, and West Virginia (USA), from 1 January 2000 through 31 December 2007 were obtained from the National Center for Health Statistics (Atlanta, GA). Data that were provided include county of residence, county of birth, birth order, trimester of first prenatal care, date of last menstrual period (LMP), gestational age, infant’s sex and birth weight, as well as maternal and paternal ages and races, and maternal education, marital status, alcohol consumption, and smoking during pregnancy. Further description of these data is available elsewhere (Bell et al. 2007b (link)).
Births with unspecified county of residence or birth, plural deliveries (e.g., twins), gestational period > 44 weeks, gestational period < 37 weeks (nonterm births), birth weight < 1,000 g or > 5,500 g, different counties of residence and delivery, or impossible gestational age and birth weight combinations were excluded from analysis (Alexander et al. 1996 (link)). Births also were excluded if LMP was missing or the estimated birth based on LMP and gestational length was > 30 days from the midday of the birth month reported on the birth certificate.
Air pollution and weather data. PM_2.5 chemical components data were obtained from the U.S. Environmental Protection Agency (EPA) Air Explorer (U.S. EPA 2010a). PM₁₀ and PM_2.5 total mass, CO, NO₂, O₃, and SO₂ data were obtained from the U.S. EPA Air Quality System for 1999–2007 (U.S. EPA 2010b). We included only counties with PM_2.5 chemical component data because these exposures are our primary focus. PM₁₀, PM_2.5, and PM_2.5 chemical components were measured every 3–6 days. Gaseous pollutants were measured daily, although O₃ was measured mainly during the warm season. Some monitors began or ceased observation during the study period. We investigated PM_2.5 chemical components identified by previous research and literature review to have potential links to health and/or contribute substantially to PM_2.5 total mass: aluminum, ammonium ion, arsenic, cadmium, calcium, chlorine, elemental carbon, lead, mercury, nickel, nitrate, organic carbon matter, silicon, sodium ion, sulfur, titanium, vanadium, and zinc (Bell et al. 2007a (link); Franklin et al. 2008 (link); Haynes et al. 2011 (link); Ostro et al. 2007 (link); Zanobetti et al. 2009 (link)).
We calculated apparent temperature (AT), a measure that reflects overall temperature discomfort (Kalkstein and Valimont 1986 ), based on daily temperature and dew point temperature data obtained from the National Climatic Data Center (2010) . If weather data were unavailable for a given county, we assigned the AT value for the closest county with weather data.
Exposure estimation. For each birth we calculated the average level of each pollutant during gestation and each trimester, and average AT during each trimester. Delivery date was estimated based on self-reported LMP and gestational length, assuming conception 2 weeks after LMP. We defined trimesters as 1–13 weeks, 14–26 weeks, and week 27 to delivery, as in previous studies (Bell et al. 2007b (link)).
Exposures were estimated based on county of residence. Not all counties had data for all pollutants. Measurements from multiple monitors in the same county on the same day were averaged to generate daily pollutant -levels. To avoid biases due to changes in measurement frequency, daily pollutant levels and AT values were combined to estimate weekly exposures, which were then averaged to estimate gestational or trimester exposure. Births for which exposure estimates were unavailable for > 25% of the weeks in any trimester for a given pollutant were excluded from analyses of that pollutant.
Statistical analysis. Each birth was cate-gorized as low or normal birth weight using clinically defined LBW (< 2,500 g). Logistic regression was used to estimate associations between LBW and gestational exposure to each pollutant with adjustment for maternal race (African American, Caucasian, other), marital status (married, unmarried), tobacco consumption during pregnancy (yes, no, unknown), alcohol consumption during pregnancy (yes, no, unknown), highest education (< 12 years, 12 years, 13–15 years, > 15 years, unknown), age (< 20, 20–24, 25–29, 30–34, 35–39, ≥ 40 years), infant sex (male, female), gestational length (37–38, 39–40, 41–42, 43–44 weeks), the trimester prenatal care began (1st, 2nd, 3rd, no care, unknown), first in birth order (yes, no, unknown), delivery method (vaginal, cesarean section, unknown), average AT for each trimester, season of birth, and year of birth. In addition we included regional indicators to adjust for local factors such as area-level socioeconomic conditions (Table 1). We conducted sensitivity analyses restricted to first births to assess the influence of multiple births by the same mother on associations (Zhu et al. 1999 (link)).
For pollutants showing statistically significant associations with LBW in single--pollutant models, we conducted two-pollutant models that included pairs of pollutants that were not highly correlated (correlation < 0.5). Similarly, for pollutants associated in single-pollutant model, we assessed effects by trimester using a model with trimesters’ exposures included simultaneously. Because trimester exposures could be correlated, we performed sensitivity analysis with trimester exposures adjusted to be orthogonal using a method we published previously (Bell et al. 2007b (link)). In brief, we predicted exposures of two trimesters using exposure level of a given trimester (reference trimester), calculated their residuals, and put them into models besides exposure of reference trimester. This approach can avoid covariance among trimester exposures. This procedure was repeated using each trimester as the reference trimester, and we have four models for trimester analysis in total (main model and three models as sensitivity -analyses). Further description of this approach is available elsewhere (Bell et al. 2007b (link)).
Additional analyses were conducted for pollutants that showed statistically robust results in two-pollutant models. We included interaction terms between gestational pollutant exposures and sex or race to investigate whether some populations are particularly susceptible, because previous analysis found higher relative risks associated with ambient air pollution in some populations than in others (Bell et al. 2007b (link)). Statistical significance was determined at an alpha level of 0.05 for the entire analyses.

In experimental exposure in the lab, Two hundred and forty Nile tilapia (30 ± 5 g b.w.) were distributed into glass aquaria containing 10% LC₅₀ of lead nitrate (PbNO₃) [34 (link)] and zinc sulfate (ZnSO₄) [35 ], 14.33 mg/L and 6.398 mg/L, respectively, for 4, 6, and 8 weeks.

All chemicals, lead nitrate (Pb(NO₃)₂), Zinc nitrate (Zn(NO₃)₂), Cadmium nitrate (Cd(NO₃)₂), Nickel nitrate (Ni(NO₃)₂), potassium permanganate (KMnO₄), Hydrogen Peroxide (H₂O₂), Sodium hydroxide (NaOH), nitric acid (HNO₃), Zinc sulfate heptahydrate (ZnSO₄.7H₂O), and potassium hydroxide (KOH) were provided by Merck company (Germany) and used without any further purification.