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Gong X, Huang Y, Duong J, Leng S, Zhan FB, Guo Y, Lin Y, Luo L. Industrial air pollution and low birth weight in New Mexico, USA. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 348:119236. [PMID: 37857221 PMCID: PMC10829484 DOI: 10.1016/j.jenvman.2023.119236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/10/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
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.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Yanhong Huang
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Jenny Duong
- New Mexico Department of Health, Santa Fe, NM, 87505, USA.
| | - Shuguang Leng
- School of Medicine, University of New Mexico, University of New Mexico Comprehensive Cancer Center, Lung Cancer Program, Lovelace Biomedical Research Institute, Albuquerque, New Mexico, 87131, USA.
| | - F Benjamin Zhan
- Department of Geography and Environmental Studies, Texas Center for Geographic Information Science, Texas State University, San Marcos, TX, 78666, USA.
| | - Yan Guo
- Department of Public Health and Sciences, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, 33136, USA.
| | - Yan Lin
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Li Luo
- Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA.
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Gong X, Liu L, Huang Y, Zou B, Sun Y, Luo L, Lin Y. A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1183. [PMID: 37695355 PMCID: PMC10829730 DOI: 10.1007/s10661-023-11814-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/30/2023] [Indexed: 09/12/2023]
Abstract
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.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Lin Liu
- Department of Computer Science, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yanhong Huang
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, 410083, Hunan, China
| | - Yeran Sun
- Department of Geography, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK
| | - Li Luo
- Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yan Lin
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
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Gong X, Zhan FB. A method for identifying critical time windows of maternal air pollution exposures associated with low birth weight in offspring using massive geographic data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:33345-33360. [PMID: 35022967 DOI: 10.1007/s11356-021-17762-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/22/2021] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
| | - Franklin Benjamin Zhan
- Department of Geography, Texas Center for Geographic Information Science, Texas State University, San Marcos, TX, 78666, USA.
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Lin Y, Hoover J, Beene D, Erdei E, Liu Z. Environmental risk mapping of potential abandoned uranium mine contamination on the Navajo Nation, USA, using a GIS-based multi-criteria decision analysis approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:30542-30557. [PMID: 32468361 PMCID: PMC7387200 DOI: 10.1007/s11356-020-09257-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/11/2020] [Indexed: 05/19/2023]
Abstract
The Navajo Nation (NN), a sovereign indigenous tribal nation in the Southwestern United States, is home to 523 abandoned uranium mines (AUMs). Previous health studies have articulated numerous human health hazards associated with AUMs and multiple environmental mechanisms/pathways (e.g., air, water, and soil) for contaminant transport. Despite this evidence, the limited modeling of AUM contamination that exists relies solely on proximity to mines and only considers single rather than combined pathways from which the contamination is a product. In order to better understand the spatial dynamics of contaminant exposure across the NN, we adopted the following established geospatial and computational methods to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination: GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP). Eight criteria layers were selected for the GIS-MCDA model: proximity to AUMs, roadway proximity, drainage proximity, topographic landforms, wind index, topographic wind exposure, vegetation index, and groundwater contamination. Model sensitivity was evaluated using the one-at-a-time method, and statistical validation analysis was conducted using two separate environmental datasets. The sensitivity analysis indicated consistency and reliability of the model. Model results were strongly associated with environmental uranium concentrations. The model classifies 20.2% of the NN as high potential for AUM contamination while 65.7% and 14.1% of the region are at medium and low risk, respectively. This study is entirely a novel application and a crucial first step toward informing future epidemiologic studies and ongoing remediation efforts to reduce human exposure to AUM waste.
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Affiliation(s)
- Yan Lin
- Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM, USA.
| | - Joseph Hoover
- Department of Social Sciences and Cultural Studies, Montana State University Billings, Billings, MT, USA
| | - Daniel Beene
- Community Environmental Health Program, College of Pharmacy, Health Sciences Center, University of New Mexico, Albuquerque, NM, USA
| | - Esther Erdei
- Community Environmental Health Program, College of Pharmacy, Health Sciences Center, University of New Mexico, Albuquerque, NM, USA
| | - Zhuoming Liu
- Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM, USA
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Aliyu YA, Botai JO. An Exposure Appraisal of Outdoor Air Pollution on the Respiratory Well-being of a Developing City Population. J Epidemiol Glob Health 2019; 8:91-100. [PMID: 30859794 PMCID: PMC7325812 DOI: 10.2991/j.jegh.2018.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 04/05/2018] [Indexed: 11/09/2022] Open
Abstract
Zaria is the educational hub of northern Nigeria. It is a developing city with a pollution level high enough to be ranked amongst the World Health Organization’s (WHO) most polluted cities. The study appraised the influence of outdoor air pollution on the respiratory well-being of a population in a limited resource environment. With the approved ethics, the techniques utilized were: portable pollutant monitors, respiratory health records, WHO AirQ+ software, and the American Thoracic Society (ATS) questionnaire. They were utilized to acquire day-time weighted outdoor pollution levels, health respiratory cases, assumed baseline incidence (BI), and exposure respiratory symptoms among selected study participants respectively. The study revealed an average respiratory illness incidence rate of 607 per 100,000 cases. Findings showed that an average of 2648 cases could have been avoided if the theoretical WHO threshold limit for the particulate matter with diameter of <2.5/10 micron (PM2.5/PM10) were adhered to. Using the questionnaire survey, phlegm was identified as the predominant respiratory symptom. A regression analysis showed that the criteria pollutant PM2.5, was the most predominant cause of respiratory symptoms among interviewed respondents. The study logistics revealed that outdoor pollution is significantly associated with respiratory well-being of the study population in Zaria, Nigeria.
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Affiliation(s)
- Yahaya A Aliyu
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa.,Department of Geomatics, Ahmadu Bello University, Zaria, Nigeria
| | - Joel O Botai
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa.,South African Weather Service, Erasmusrand, Pretoria, South Africa
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Gong X, Lin Y, Bell ML, Zhan FB. Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA. ENVIRONMENT INTERNATIONAL 2018; 120:181-198. [PMID: 30096612 DOI: 10.1016/j.envint.2018.07.045] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 07/29/2018] [Accepted: 07/29/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Most previous studies examining associations between maternal exposures to air pollutants during pregnancy and low birth weight (LBW) in offspring focused on criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, CO, and Pb). The relationship between non-criteria air pollutants and LBW is understudied and requires greater coverage. OBJECTIVES This study investigated associations between maternal residential exposure to industrial air pollutants during pregnancy and LBW in offspring. METHODS This study used a case-control study design that included 94,106 term LBW cases and 376,424 controls. It covered 78 air pollutants common to both the Toxics Release Inventory (TRI) and ground air quality monitoring databases in Texas during 1996-2008. A modified version of the Emission Weighted Proximity Model (EWPM), calibrated with ground monitoring data, was used to estimate maternal residential exposure to industrial air pollutants during pregnancy. Binary logistic regression analyses were performed to calculate odds ratios (ORs) reflecting the associations of maternal exposure to industrial air pollutants and LBW in offspring, adjusted for child's sex, gestational weeks, maternal age, education, race/ethnicity, marital status, prenatal care, tobacco use during pregnancy, public health region of maternal residence, and year of birth. In addition, the Bonferroni correction for multiple comparisons was applied to the results of logistic regression analysis. RESULTS Relative to the non-exposed reference group, maternal residential exposure to benzene (adjusted odds ratio (aOR) 1.06, 95% confidence interval (CI) 1.04, 1.08), benzo(g,h,i)perylene (aOR 1.04, 95% CI 1.02, 1.07), cumene (aOR 1.05, 95% CI 1.03, 1.07), cyclohexane (aOR 1.04, 95% CI 1.02, 1.07), dichloromethane (aOR 1.04, 95% CI 1.03, 1.07), ethylbenzene (aOR 1.05, 95% CI 1.03, 1.06), ethylene (aOR 1.06, 95% CI 1.03, 1.09), mercury (aOR 1.04, 95% CI 1.02, 1.07), naphthalene (aOR 1.03, 95% CI 1.01, 1.05), n-hexane (aOR 1.06, 95% CI 1.04, 1.08), propylene (aOR 1.06, 95% CI 1.03, 1.10), styrene (aOR 1.06, 95% CI 1.04, 1.08), toluene (aOR 1.05, 95% CI 1.03, 1.07), and zinc (fume or dust) (aOR 1.10, 95% CI 1.06, 1.13) was found to have significantly higher odds of LBW in offspring. When the estimated exposures were categorized into four different groups (zero, low, medium, and high) in the analysis, eleven of the fourteen air pollutants, with the exception of benzo(g,h,i)perylene, ethylene, and propylene, remained as significant risk factors. CONCLUSIONS Results indicate that maternal residential proximity to industrial facilities emitting any of the fourteen pollutants identified by this study during pregnancy may be associated with LBW in offspring. With the exception of benzene, ethylbenzene, toluene, and zinc, the rest of the fourteen air pollutants are identified as LBW risk factors for the first time by this study. Further epidemiological, biological, and toxicological studies are suggested to verify the findings from this study.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Yan Lin
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Michelle L Bell
- School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA.
| | - F Benjamin Zhan
- Texas Center for Geographic Information Science, Department of Geography, Texas State University, San Marcos, TX 78666, USA.
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Gong X, Lin Y, Zhan FB. Industrial air pollution and low birth weight: a case-control study in Texas, USA. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:30375-30389. [PMID: 30159842 DOI: 10.1007/s11356-018-2941-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 08/08/2018] [Indexed: 06/08/2023]
Abstract
Many studies have investigated associations between maternal residential exposures to air pollutants and low birth weight (LBW) in offspring. However, most studies focused on the criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, CO, and Pb), and only a few studies examined the potential impact of other air pollutants on LBW. This study investigated associations between maternal residential exposure to industrial air emissions of 449 toxics release inventory (TRI) chemicals and LBW in offspring using a case-control study design based on a large dataset consisting of 94,106 LBW cases and 376,424 controls in Texas from 1996 to 2008. Maternal residential exposure to chemicals was estimated using a modified version of the emission-weighted proximity model (EWPM). The model takes into account reported quantities of annual air emission from industrial facilities and the distances between the locations of industrial facilities and maternal residence locations. Binary logistic regression was used to compute odds ratios measuring the association between maternal exposure to different TRI chemicals and LBW in offspring. Odds ratios were adjusted for child's sex, birth year, gestational length, maternal age, education, race/ethnicity, and public health region of maternal residence. Among the ten chemicals selected for a complete analysis, maternal residential exposures to five TRI chemicals were positively associated with LBW in offspring. These five chemicals include acetamide (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.24, 4.20), p-phenylenediamine (aOR 1.63, 95% CI 1.18, 2.25), 2,2-dichloro-1,1,1-trifluoroethane (aOR 1.41, 95% CI 1.20, 1.66), tributyltin methacrylate (aOR 1.20, 95% CI 1.06, 1.36), and 1,1,1-trichloroethane (aOR 1.11, 95% CI 1.03, 1.20). These findings suggest that maternal residential proximity to industrial air emissions of some chemicals during pregnancy may be associated with LBW in offspring.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yan Lin
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM, 87131, USA
| | - F Benjamin Zhan
- Texas Center for Geographic Information Science, Department of Geography, Texas State University, San Marcos, TX, 78666, USA.
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