Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA

Residential proximity to toxic metal-emitting industrial sites and toenail metal concentrations in a United States-wide prospective cohort

BACKGROUND

Industrial facilities across the United States (US) release millions of pounds of toxic chemicals, including metals. Exposure to toxic metals has been associated with adverse health outcomes, but there is limited evidence on the association between living near metal-releasing facilities and the body burden of emitted compounds.

OBJECTIVE

To investigate the association between residential proximity to toxic metal-emitting industrial facilities and toenail metal concentrations and to evaluate whether associations differed by race.

METHODS

In a sample of 1556 non-Hispanic Black (32.5%) and non-Hispanic White (67.5%) women from the Sister Study, we used the US Environmental Protection Agency Toxics Release Inventory to identify metal-emitting facilities within 3, 5, and 10 km of participants' baseline residences. We measured toenail concentrations (μg/g) of arsenic, cadmium, cobalt, chromium, and lead. Using multivariable linear regression, we examined associations between residential proximity to and emissions from metal-emitting facilities and toenail metal concentrations, stratifying by race. We explored modification of race-stratified associations by neighborhood deprivation, using the Area Deprivation Index (ADI).

RESULTS

Black participants were more likely to reside within 3 km of chromium-releasing facilities and 5 and 10 km of all observed metal-emitting sites. Living near metal-releasing facilities was not associated with higher toenail metal concentrations overall. Among Black women, higher chromium emissions exposure was associated with higher toenail chromium levels (βTertile3vs.non-exposed = 2.36 μg/g, 95% CI = 0.63, 4.10). An association with lead was observed among Black women residing in the most deprived areas (≥75th ADI percentile: β = 3.08 μg/g, 95% CI = 1.46, 4.71). No associations were observed for White participants.

CONCLUSIONS

Despite low exposure prevalence, our findings suggest that living near chromium- and lead-releasing facilities, especially at shorter distances, may be associated with higher corresponding toenail metal levels among Black women, particularly those residing in the most disadvantaged areas.

Does Exposure to Ambient Air Pollution Affect Gestational Age and Newborn Weight?-A Systematic Review

Current evidence suggests that airborne pollutants have a detrimental effect on fetal growth through the emergence of small for gestational age (SGA) or term low birth weight (TLBW). The study's objective was to critically evaluate the available literature on the association between environmental pollution and the incidence of SGA or TLBW occurrence. A comprehensive literature search was conducted across Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar using predefined inclusion and exclusion criteria. The methodology adhered to the PRISMA guidelines. The systematic review protocol was registered in PROSPERO with ID number: CRD42022329624. As a result, 69 selected papers described the influence of environmental pollutants on SGA and TLBW occurrence with an Odds Ratios (ORs) of 1.138 for particulate matter ≤ 10 μm (PM10), 1.338 for particulate matter ≤ 2.5 μm (PM2.5), 1.173 for ozone (O3), 1.287 for sulfur dioxide (SO2), and 1.226 for carbon monoxide (CO). All eight studies analyzed validated that exposure to volatile organic compounds (VOCs) is a risk factor for SGA or TLBW. Pregnant women in the high-risk group of SGA occurrence, i.e., those living in urban areas or close to sources of pollution, are at an increased risk of complications. Understanding the exact exposure time of pregnant women could help improve prenatal care and timely intervention for fetuses with SGA. Nevertheless, the pervasive air pollution underscored in our findings suggests a pressing need for adaptive measures in everyday life to mitigate worldwide environmental pollution.

Assessing chronic gestational exposure to environmental chemicals in pregnant women: Advancing the co-PBK model

Vulnerable populations, such as pregnant women and their fetuses, confront potential health risks due to exposure to environmental toxic compounds. Computational methods have been popular in assessing chemical exposure to populations, contrasting with traditional cohort studies for human biomonitoring. This study proposes a screening-level approach based on physiologically based kinetic (PBK) modeling to evaluate the steady-state exposure of pregnant women to environmental chemicals throughout pregnancy. To exemplify the modeling application, naphthalene was chosen. Simulation results indicated that maternal fat exhibited significant bioaccumulation potential, with the log-transformed BTF of naphthalene at 0.51 mg kg-1 per mg d-1 in the steady state. The placenta was primarily exposed to 0.83 mg/d naphthalene for a 75.2 kg pregnant woman, considering all exposure routes. In the fetal structure, single-organ fetal PBK modeling estimated a naphthalene exposure of 123.64 mg/d to the entire fetus, while multiple-organ fetal PBK modeling further revealed the bioaccumulation highest in fat tissue. The liver identified as the vital organ for metabolism, kBioT,LiverM was demonstrated with the highest sensitivity among rate constants in the maternal body. Furthermore, the first-order kinetic rate constants related to the placenta and blood were found to impact the distribution process of naphthalene in the fetus, influencing gestational exposure. In conclusion, urgent attention is needed to develop a computational biomonitoring tool for assessing toxic chemical exposure in vulnerable populations.

Industrial air pollution and low birth weight in New Mexico, USA

In recent decades, the low birth weight (LBW) rate in New Mexico has consistently exceeded the Unites States average. Maternal exposure to air pollution during pregnancy may be a significant contributor to LBW in offspring. This study investigated the links between maternal residential exposure to air pollution from industrial sources and the risk of LBW in offspring. The analysis included 22,375 LBW cases and 233,340 controls. It focused on 14 common chemicals listed in the Toxic Release Inventory (TRI) and monitoring datasets, which have abundant monitoring samples. The Emission Weighted Proximity Model (EWPM) was used to calculate maternal air pollution exposure intensity. Adjusted odds ratios (adjORs) were calculated using binary logistic regressions to examine the association between maternal residential air pollution exposure and LBW, while controlling for potential confounders, such as the maternal age, race/ethnicity, gestational age, prenatal care, education level, consumption of alcohol during pregnancy, public health regions, child's sex, and the year of birth. Multiple comparison correction was applied using the False Discovery Rate approach. The results showed that maternal residential exposure to 1,2,4-trimethylbenzene, benzene, chlorine, ethylbenzene, and styrene had significant positive associations with LBW in offspring, with adjusted odds ratios ranging from 1.10 to 1.13. These five chemicals remained as significant risk factors after dividing the estimated exposure intensities into four categories. In addition, significant linear trends were found between LBW and maternal exposure to each of the five identified chemicals. Furthermore, 1,2,4-trimethylbenzene was identified as a risk factor to LBW for the first time. The findings of this study should be confirmed through additional epidemiological, biological, and toxicological studies.

Traffic-related air pollution is associated with spontaneous extremely preterm birth and other adverse perinatal outcomes

BACKGROUND

Although there is growing awareness of the relationship between air pollution and preterm birth, limited data exist regarding the relationship with spontaneous preterm birth and severe neonatal outcomes.

OBJECTIVE

This study aimed to examine the association between traffic-associated air pollution exposure in pregnancy and adverse perinatal outcomes including extremes of preterm birth, neonatal intensive care unit admissions, low birthweight, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation.

STUDY DESIGN

This was a retrospective cohort study of singleton pregnancies of patients residing in a metropolitan area in the southern United States. Using monitors strategically located across the region, average nitrogen dioxide concentrations were obtained from the Environmental Protection Agency Air Quality System database. For patients living within 10 miles of a monitoring station, average exposure to nitrogen dioxide was estimated for individual patients' pregnancy by trimester. Logistic regression models were used to assess the effect of pollutant exposure on gestational age at birth, indicated vs spontaneous delivery, and neonatal outcomes while adjusting for maternal age, self-reported race, parity, season of conception, diabetes mellitus, body mass index, registered Health Equity Index, and nitrogen dioxide monitor region. Adjusted odds ratios and 95% confidence intervals were calculated for an interquartile increase in average nitrogen dioxide exposure.

RESULTS

Between January 1, 2013 and December 31, 2021, 93,164 patients delivered a singleton infant. Of these, 62,189 had measured nitrogen dioxide exposure during the pregnancy from a nearby monitoring station. Higher average nitrogen dioxide exposure throughout pregnancy was significantly associated with preterm birth (adjusted odds ratio, 1.94; 95% confidence interval, 1.77-2.12) and an increase in neonatal intensive care unit admissions, low birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation. This relationship persisted for nulliparous patients and spontaneous preterm birth, and had a greater association with earlier preterm birth.

CONCLUSION

In a metropolitan area, increased exposure to the air pollutant nitrogen dioxide in pregnancy was associated with spontaneous preterm birth and had a greater association with extremely preterm birth. A greater association with neonatal intensive care unit admissions, low-birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation was found even in term infants.

A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.

Maternal exposure to metal components of PM2.5 and low birth weight in New Mexico, USA

Infants with low birth weight (LBW) are more likely to have health problems than normal weight infants. In studies examining the associations between particulate matter (PM) exposures and LBW, there is a tendency to focus on PM2.5 as a whole. However, insufficient information is available regarding the effects of different components of PM2.5 on birth weight. This study identified the associations between maternal exposure to 10 metal components of PM2.5 and LBW in offspring based on small area (divided by population size) level data in New Mexico, USA, from 2012 to 2016. This study used a pruned feed-forward neural network (pruned-FNN) approach to estimate the annual average exposure index to each metal component in each small area. The linear regression model was employed to examine the association between maternal PM2.5 metal exposures and LBW rate in small areas, adjusting for the female percentage and race/ethnicity compositions, marriage status, and educational level in the population. An interquartile range increase in maternal exposure to mercury and chromium of PM2.5 increased LBW rate by 0.43% (95% confidence interval (CI): 0.18-0.68%) and 0.63% (95% CI: 0.15-1.12%), respectively. These findings suggest that maternal exposure to metal components of air pollutants may increase the risk of LBW in offspring. With no similar studies in New Mexico, this study also posed great importance because of a higher LBW rate in New Mexico than the national average. These findings provide critical information to inform further epidemiological, biological, and toxicological studies.

Maternal exposure to metal components of PM2.5 and low birth weight in New Mexico, USA

Infants with low birth weight (LBW) are more likely to have health problems than normal weight infants. In studies examining the associations between particulate matter (PM) exposures and LBW, there is a tendency to focus on PM 2.5 as a whole. However, insufficient information is available regarding the effects of different components of PM 2.5 on birth weight. This study identified the associations between maternal exposure to 10 metal components of PM 2.5 and LBW in offspring based on small area (divided by population size) level data in New Mexico, USA, from 2012 to 2016. This study used a pruned feed-forward neural network (pruned-FNN) approach to estimate the annual average exposure index to each metal component in each small area. The linear regression model was employed to examine the association between maternal PM 2.5 metal exposures and LBW rate in small areas, adjusting for the female percentage and race/ethnicity compositions, marriage status and educational level in the population. An interquartile range increase in maternal exposure to mercury and chromium of PM 2.5 increased LBW rate by 0.43% (95% confidence interval (CI): 0.18%-0.68%) and 0.63% (95% CI: 0.15%-1.12%), respectively. These findings suggest that maternal exposure to metal components of air pollutants may increase the risk of LBW in offspring. With no similar studies in New Mexico, this study also posed great importance because of a higher LBW rate in New Mexico than the national average. These findings provide critical information to inform further epidemiological, biological, and toxicological studies.

Effects of Particulate Matter on the Risk of Gestational Hypertensive Disorders and Their Progression

Associations between particulate matter (PM) and gestational hypertensive disorders (GHDs) are well documented, but there is no evidence on the associations between PM and GHD progression, especially among those with assisted reproductive technology (ART) conceptions. To explore the effects of PM on the risk of GHDs and their progression among pregnant women with natural or ART conception, we enrolled 185,140 pregnant women during 2014-2020 in Shanghai and estimated the associations during different periods using multivariate logistic regression. During the 3 months of preconception, 10 μg/m³ increases in PM concentrations were associated with increased risks of gestational hypertension (GH) (PM_2.5: aOR = 1.076, 95% CI: 1.034-1.120; PM₁₀: aOR = 1.042, 95% CI: 1.006-1.079) and preeclampsia (PM_2.5: aOR = 1.064, 95% CI: 1.008-1.122; PM₁₀: aOR = 1.048, 95% CI: 1.006-1.092 ) among women with natural conception. Furthermore, for women with ART conceptions who suffered current GHD, 10 μg/m³ increases in PM concentrations in the third trimester elevated the risk of progression (PM_2.5: aOR = 1.156, 95% CI: 1.022-1.306 ; PM₁₀: aOR = 1.134, 95% CI: 1.013-1.270). In summary, women with natural conception should avoid preconceptional PM exposure to protect themselves from GH and preeclampsia. For women with ART conceptions suffering from GHD, it is necessary to avoid PM exposure in late pregnancy to prevent the disease from progressing.

Industrial Air Pollution Leads to Adverse Birth Outcomes: A Systematized Review of Different Exposure Metrics and Health Effects in Newborns

Objectives: To review the evidence of associations between adverse birth outcomes (ABO) and industrial air pollution.

Methods: Searches were conducted in PubMed, and Scopus databases, and additional articles were found from snowball search techniques. The included studies feature a study population of mothers with live-born babies exposed to industrial air pollutants, and they examine the effects of industrial pollutants on adverse birth outcomes—namely, low birth weight, term low birth weight, preterm birth, and small for gestational age.

Results: Altogether, 45 studies were included in this review. Exposure to PM2.5, PAHs, benzene, cadmium, and mixtures of industrial air pollutants and living near an industrial area affect birth outcomes.

Conclusion: This study concludes that industrial air pollution is an important risk factor for ABO, especially low birth weight and preterm birth. The strongest evidence is associations between ABO and air pollution from power plants and petrochemical industries. Understanding of specific chemicals that are critical to birth outcomes is still vague. However, the evidence is strongest for more specific air pollutants from the industry, such as PAH, benzene, BTEX, and cadmium.

Birth weight following pregnancy wildfire smoke exposure in more than 1.5 million newborns in Brazil: A nationwide case-control study

Background

Air pollution exposure has been associated with critical neonatal morbidities, including low birth weight (LBW). However, little is known on short-term exposure to wildfire smoke and LBW. In this study, we estimated the association between birth weight following pregnancy and wildfire smoke exposure in more than 1.5 million newborns in Brazil (considered as a very fire-prone region worldwide).

Methods

We applied a logistic regression model to estimate the percent variation in newborns with low birth weight when exposed to wildfire in different trimesters of the pregnancy.

Findings

After adjusting the model with relevant covariates, we found that an increase of 100 wildfire records in Brazil was associated with an increase in low birth weight in the Midwest region [0.98% (95%CI:0.34; 1.63)] and in the South region [18.55% (95%CI:13.66; 23.65)] when the exposure occurred in the first trimester of pregnancy.

Interpretation

Wildfires were associated with LBW and this should be of public health concern for policymakers.

Funding

Brazilian Agencies National Council for Scientific and Technological Development (CNPq); Ministry of Science, Technology and Innovation in Brazil (MCTI); and Novo Nordisk Foundation Challenge Programme.

A method for identifying critical time windows of maternal air pollution exposures associated with low birth weight in offspring using massive geographic data

Associations between maternal exposures to air pollutants and low birth weight (LBW) in offspring varied when different exposure windows were considered. Methods used in previous studies lacked flexibility in delineating exposure windows and did not consider time periods before conception, which may restrict the discoveries of critical exposure windows. This study introduces a novel method to identify critical windows of maternal air pollution exposures associated with LBW in offspring using massive georeferenced data. Through a case-control study based on birth data (94,106 LBW cases and 376,424 controls) and air quality monitoring data (367 chemicals) in Texas during 1996-2008, this study used the average ambient concentration measured by the monitoring site closest to the residence location of a mother during a time window as the maternal exposure to a specific chemical during that exposure window. Binary logistic regression was utilized to estimate air pollutant-LBW associations in different exposure windows. The odds ratios (ORs) were adjusted for child's sex, gestational weeks, maternal age, race/ethnicity, and education. The adjusted ORs were plotted against the exposure window series of different sizes for each chemical, aiming at interactively visualizing and exploring the critical exposure windows across multiple temporal scales. This study identifies ten chemicals and seventeen corresponding critical exposure windows where strong air pollutant-LBW associations are detected. The ten identified chemicals are benzaldehyde, sum of Photochemical Assessment Monitoring Stations (PAMS) target compounds, n-undecane, m-tolualdehyde, organic carbon fraction 2 (OC2), ethylene dibromide, valeraldehyde, propionaldehyde, 4-methyl-1-pentene, and zirconium. Nine critical exposure windows involving six chemicals start more than five months prior to conception, seven windows involving five chemicals commence in the second and/or third trimester of pregnancy, and the remaining one window is located in other time periods. The novel method reveals a number of critical time windows of maternal exposure to ten chemicals that are positively associated with LBW in offspring. These ten chemicals were identified as LBW risk factors for the first time. Additional studies with more data are needed to validate the results in the future.

A spatial analysis of birth defects in Texas, 1999-2011

BACKGROUND

The etiologies of major birth defects are still unclear and few spatial analyses have been conducted in the United States. Spatial analyses of individual-level data can help elucidate environmental and social risk factors.

METHODS

We used generalized additive models to analyze 52,955 cases of neural tube defects, congenital heart defects (CHDs), gastroschisis, and orofacial cleft defects, and sampled from 642,399 controls born between 1999 and 2011 in Texas. The effect of geographic location was measured using a bivariable smooth term of geocoded birth address within a logistic regression framework. We calculated and mapped odds ratios (ORs) and 95% confidence intervals (CIs) for birth defects subtypes across Texas, and adjusted for maternal characteristics, environmental indicators, and community-level covariates. We also performed time-stratified spatiotemporal analyses for more prevalent birth defects.

RESULTS

Location was significantly associated with crude odds of all birth defects except hypoplastic left heart syndrome. After adjusting for maternal characteristics, environmental indicators, and community-level factors, ORs in many geographic areas were no longer statistically significant for most defects, especially CHDs. However, areas of significant and insignificant elevated risk remained for defects in all groups in North and South Texas, with ORs for ventricular septal defects increasing over time. Low risk of birth defects was often present in the northern part of East Texas.

CONCLUSION

Significant spatial patterns of birth defects were identified and varied depending on adjustment of different categories of covariates. Further investigation of areas with increased risks may aid in our understanding of birth defects.

Health Effects of Naphthalene Exposure: A Systematic Evidence Map and Analysis of Potential Considerations for Dose-Response Evaluation

BACKGROUND

Naphthalene is a polycyclic aromatic hydrocarbon that has been associated with health effects, including cancer. As the state of the science on naphthalene toxicity continues to evolve, updated toxicity reference value(s) may be required to support human health risk assessment.

OBJECTIVES

We present a systematic evidence map of studies that could be used to derive toxicity reference value(s) for naphthalene.

METHODS

Human and animal health effect studies and physiologically based pharmacokinetic (PBPK) models were identified from a literature search based on populations, exposures, comparators, and outcomes (PECO) criteria. Human and animal studies meeting PECO criteria were refined to a smaller subset considered most informative for deriving chronic reference value(s), which are preferred for assessing risk to the general public. This subset was evaluated for risk of bias and sensitivity, and the suitability of each study for dose-response analysis was qualitatively assessed. Lowest observed adverse effect levels (LOAELs) were extracted and summarized. Other potentially relevant studies (e.g., mechanistic and toxicokinetic studies) were tracked as supplemental information but not evaluated further. Existing reference values for naphthalene are also summarized.

RESULTS

We identified 26 epidemiology studies and 16 animal studies that were considered most informative for further analysis. Eleven PBPK models were identified. The available epidemiology studies generally had significant risk of bias and/or sensitivity concerns and were mostly found to have low suitability for dose-response analysis due to the nature of the exposure measurements. The animal studies had fewer risk of bias and sensitivity concerns and were mostly found to be suitable for dose-response analysis.

CONCLUSION

Although both epidemiological and animal studies of naphthalene provide weight of evidence for hazard identification, the available animal studies appear more suitable for reference value derivation. PBPK models and mechanistic and toxicokinetic data can be applied to extrapolate these animal data to humans, considering mode of action and interspecies metabolic differences. https://doi.org/10.1289/EHP7381.

Linking metal (Pb, Hg, Cd) industrial air pollution risk to blood metal levels and cardiovascular functioning and structure among children in Syracuse, NY

BACKGROUND

Exposure to air pollution has been linked to individual health effects in occupational environments and communities proximate to air pollution sources. Use of estimated chemical concentrations from the Risk Screening Environmental Indicators (RSEI) model, derived from the Toxics Release Inventory, can help approximate some contributions to individual lifetime exposure to risk from air pollution and holds potential for linkages with specific health outcome data.

OBJECTIVES

Our objectives were: (1) use regression modeling to test for associations between observed blood metal concentrations in children and RSEI total air concentrations of the same metals released from proximate manufacturing facilities; (2) determine the relative contribution of RSEI air pollution to blood metal concentrations; and (3) examine associations between chronic metal exposure and cardiovascular functioning and structure in study participants.

METHODS

Using data synthesis methods and regression modeling we linked individual blood-based levels of lead, mercury, and cadmium(Pb, Hg, Cd) and cardiovascular functioning and structure to proximate industrial releases of the same metals captured by the Environmental Protection Agency's (EPA) RSEI geographic microdata.

RESULTS

We found that RSEI-derived ground-level ambient air concentrations of Hg and Cd were a significant predictor of blood metal levels, when controlling for covariates and other exposure variables. In addition to associations with blood metal findings, RSEI concentrations also predicted cardiovascular dysfunction and risk including changes in left-ventricular mass, blood pressure, and heart rate.

DISCUSSION

Right-to-know data, such as EPA's RSEI, can be linked to objective health outcomes, rather than simply serving as a non-specific risk estimate. These data can serve as a proxy for hazard exposure and should be used more widely to understand the dynamics of environmental exposure. Furthermore, since these data are both a product of and contribute to regulatory decision making, they could serve as an important link between disease risk and translation-orientated national environmental health policy.

Determination of the most effective control methods of SO2 Pollution in Tehran based on adaptive neuro-fuzzy inference system

Air pollution in metropolises is one of the serious problems of human life. Tehran is one of the cities facing air pollution problem. Urban managers concern about choosing different management methods to control air pollution. In this study, a combination of fuzzy systems and neural networks has been used to select the most suitable scenario for controlling SO₂ pollution. According to the method presented in this paper, 8 input data categories such as wind speed, precipitation, temperature, pressure, humidity, gas oil consumption, gasoline consumption and urban green space levels have been used as independent parameters and SO₂ pollutant concentration has been considered as the dependent parameter. The contribution of each meteorological station to the meteorological data was determined by Thiessen Polygon Method. Then, using adaptive neural fuzzy inference systems, modeling was done in Sugeno Method and the least root mean square error (3.19) was determined for the model. Then, by changing each of the independent parameters, the effect of each of these independent parameters on SO₂ pollutant was measured. The results showed that the parameters of pressure, urban green space, gasoline consumption, gas oil consumption, temperature, wind speed and humidity, respectively, had the greatest effect on reducing the SO₂ concentration. Since the parameters of gasoline and gas oil consumption as well as the area of green space are changeable by different policies and by human decisions, the concentration of SO₂ pollutant can be controlled by reducing the consumption of gasoline and gas oil and increasing the green space in Tehran.

Association of Ambient air Pollution with risk of preeclampsia during pregnancy: a retrospective cohort study

BACKGROUND

Ambient air pollution is becoming a serious environmental problem in China. The results were inconsistent on that air pollution was a risk factor of preeclampsia in pregnancy.

METHODS

Total 116,042 pregnant women were enrolled from 22 hospitals in 10 cities of Hebei Province, China from January 1, 2015 to December 31, 2017. The parturients were divided into preeclampsia group (PE group) and non-preeclampsia group (non-PE group). The data of air pollutants, namely, particulate matter (PM)2.5, PM10, NO2, SO2, CO, O3 were collected from China Environmental Inspection Station.

RESULTS

Among the 116,042 pregnant women, 2988 (2.57%) pregnant women were diagnosed with preeclampsia. The concentrations of exposed PM2.5, PM10, NO2 and O3 in the PE group were significantly higher than those in the non-PE group, and they were risk factors of the PE group in the first and second trimester of pregnancy respectively. The concentrations of exposed SO2 and CO in PE patients and non-PE women were not different, but high concentration of these air pollutants were risk factors to PE in the second trimester.

CONCLUSION

The exposure to PM2.5, PM10, NO2, O3 were risk factors for preeclampsia in the first and second trimester of pregnancy, while only at high level, SO2 and CO were risk factors for preeclampsia in the second trimester of pregnancy.

Space-time hot spots of critically ill small for gestational age newborns and industrial air pollutants in major metropolitan areas of Canada

We assessed the association of spatiotemporal hot spots of critically ill small for gestational age (ciSGA) newborns and industrial air emissions. Using neonatal admission data from the Canadian Neonatal Network between 2006 and 2010 (n = 32,836 infants), we aggregated maternal residential postal codes from nineteen census metropolitan areas (CMA) into space-time cubes and applied emerging hot spot analyses. Using National Pollutant Release Inventory data (n = 161 chemicals) and Environment Canada weather station data (n = 19 sites), we estimated monthly wind-dispersion of air emissions and calculated hot spots. We associated the patterns using logistic regression, with covariates for low socioeconomic status, NO₂ pollution, and number of infants. A total of 5465 infants were identified as ciSGA and the larger CMAs had more and larger hot spots (i.e. accumulation of events in space and time). Seventy-eight industrial chemical hot spots were associated with ciSGA hot spots. The highest number of positive associations were for 28 different pollutants, which differed by CMA. Twenty-one were known or suspected developmental toxicants, such as particulate matter, carbon monoxide, heavy metals, and volatile organic compounds. Associations with hot spots of industrial chemical emissions were geographically specific and may help explain the space-time trends of ciSGA.

Source-related components of fine particulate matter and risk of adverse birth outcomes in Northern Italy

BACKGROUND/AIM

The aim of the present study was to assess the association between PM_2.5, its sources, and preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) in a large open residential cohort (Supersito Project in the Emilia-Romagna Region - Northern Italy).

METHODS

We collected 2012-2014 pregnancy and childbirth data from Birth Assistance Certificates and selected the pregnancies of interest. PTBs (gestational age < 37 weeks), LBW (weight < 2500 g), and SGA (newborns weighing ≤ 10th age and pregnancy week-specific percentile) were considered. Three-year measurements of daily concentrations and constituents of PM_2.5 were available at four sites and were analyzed through a source apportionment approach identifying 6 sources (traffic, biomass burning, oil combustion, anthropogenic mix, and two secondary factors). Exposure to PM_2.5 and sources was calculated at address level. Using logistic regression models, associations between exposure and outcomes were derived, applying single-pollutant and two-pollutant models, to verify the independent effect of each source.

RESULTS

The study included 23,708 neonates born to 23,415 women, among whom 1,311 PTB, 424 LBW, and 1,354 SGA occurred. PTB was the only outcome associated with PM_2.5 mass (OR 1.03, 95% CI 1.002-1.058 per 1 μg/m³). Traffic, oil combustion and secondary sulfates and organics showed independent effects on PTB. Exposure to secondary nitrates was associated with a lower risk of PTB. There was no association between LBW or SGA and source-specific PM_2.5 components or the residual PM_2.5 related to all other sources.

CONCLUSION

This study found an association between PTB and PM_2.5. Traffic, secondary sulfates, and organic and oil combustion were the sources with most consistent association.

A review and perspective of recent research in biological treatment applied in removal of chlorinated volatile organic compounds from waste air

Chlorinated volatile organic compounds (Cl-VOCs) waste air is a kind of typical recalcitrant organic compounds, which poses a great threat to the ecological environment and human health. At present, the biotechnology is considered as a potential strategy for the Cl-VOCs removal due to the advantages of low energy consumption and less possibility of secondary pollution. This work summarizes the recent researches on strains, bioreactors and technology integration. The dominant pure strains for biodegradation of Cl-VOCs are first outlined with a special focus on the co-metabolism of multi-components. It then summarizes two bioreactors (optimized airlift reactor (ALR) and two-phase partitioning bioreactor (TPPB)) and strategy (addition of surfactant) for improvement of biotrickling filter (BTF), which are benefit to achieve the mass transfer enhancement in the removal of hydrophobic Cl-VOCs from waste air. After that, the integration technologies, such as magnetic field (MF)-BTF, non-thermal plasma (NTP)/ultraviolet light (UV)-BTF, and microbial electrolytic cells (MEC), are elucidated, which provide opportunities for complete mineralization of Cl-VOCs in a more efficient, energy-saving and economical way. Finally, current challenges and a perspective of future research on biotechnology for Cl-VOCs removal are thoroughly discussed.

Valuation of Estimation Toxic Chemical Release Inventory Method-Focusing on Paint Manufacturing Process

Industrial chemicals differ in their treatment methods and types, depending on their physicochemical properties. Highly volatile chemicals are emitted despite installation of preventive facilities, such as scrubbers and adsorption towers. Some countries release a Toxic Release Inventory (TRI), which is a mandatory report on the amount of chemicals emitted annually. This report is released to the citizens to ensure their right to knowledge and life. Numerous methods have been devised to investigate the amount of chemical emissions. There are four methods to estimate TRI emissions (Emission Factor Method; Material Balance Method; Source Testing Method; Emission Model Method). Moreover, efforts have been made to increase awareness and formulate plans to reduce chemical emissions. Despite this, the TRI method tends to underestimate and overestimate, especially due to volatile compounds. If the results of the TRI emissions are underestimated, toxic chemicals can have a negative impact on citizens. Volatile compounds are commonly used in chemical manufacturing plants, such as paint plants. In this study, a suitable method for each industrial process was suggested based on conservative estimates of multiple toxic chemical inventory method, focusing on the paint manufacturing process. In the paint manufacturing plant, storage, weighing, and mixing processes should be used emission model method to estimate TRI. In the reaction process, TRI must be estimated by the source test method. In the transfer process, the emission factor method should be used to estimate TRI. In the atmosphere prevention process, the emission factor method or source testing method should be used depending on the physical and chemical properties such as vapor pressure of the chemical.

Effect of ambient air pollution on premature SGA in Changzhou city, 2013-2016: a retrospective study

Background

Air pollution is becoming an increased burden to the world. Previous studies have confirmed its effects on adverse birth outcomes, but few associated with premature small for gestational age (SGA). We report a retrospective cohort study conducted in Changzhou city to evaluate the association between air pollutants (PM_2.5, SO₂ and NO₂) and premature SGA during pregnancy.

Methods

A total of 46,224 births were collected from January, 2013 to December, 2016, in Changzhou Maternity and Child Health Care Hospital, finally 2709 preterm births were admitted for study. Corresponding air monitoring data were collected from Changzhou Environmental Protection Agency. Generalized estimating equations were used to examine the association between these air pollutants and premature SGA controlling for individual covariates in single- and multi-pollutant models.

Results

We found that, in the third trimester, every 10 μg/m³ increments in PM_2.5 concentration were associated with premature SGA (OR = 1.18, 95% CI: 1.03–2.83; OR = 1.37, 95% CI: 1.03–3.58) in two- and three-pollutants models. In the whole gestation, a 10 μg/m³ increment in PM_2.5 concentration in two- and three-pollutant models were related to premature SGA (OR = 1.53, 95% CI: 1.38–2.47; OR = 1.73, 95% CI: 1.18–2.57). The OR (95% CI) of premature SGA were increasing across quintiles of PM_2.5, SO₂, NO₂ concentrations during the whole gestation period adjusting for confounders (P_{for trend} < 0.001).

Conclusions

These results indicated that pregnant women exposed to PM_2.5, combined with other pollutants in the third trimester have a higher risk to deliver premature SGA babies, providing further evidence linking PM_2.5 and pregnancy outcomes.