Environmental pollution is a global problem and the subject of increasing worldwide public health concern.1 In particular, air pollution is regarded as the largest single environmental risk to health. More than 80% of people living in urban areas that monitor air pollution are exposed to air quality levels that exceed the WHO limits, and all regions of the world are affected. Declines in urban air quality increase the risk of cerebrovascular accidents, coronary artery disease, lung carcinoma, and chronic and acute respiratory diseases (eg, asthma, obstructive lung disease, and acute lower respiratory infections).
Wetlands, the biological filters of the Earth, play an important role in biochemical transformation of various pollutants. Wetland plants, in this direction, help in accumulating various contaminants from aquatic bodies. Considering this, the present study was planned to estimate different metals (Cd, Cu, Cr, Co, Fe, Pb, Zn, and Mn) in water, sediment, soil, and plant (4 aquatic and 12 terrestrial) samples of Kanjli wetland, Kapurthala, Punjab (India), and a Ramsar site. It was observed that the contents of Cd and Pb in water samples were higher than limits prescribed by Bureau of Indian standards. Bioaccumulation and translocation factors for various metals were also calculated. Although all the plant species were found to be hyperaccumulator for one or the other metal studied, maximum six metals (Cd, Co, Fe, Mn, Pb, and Zn) were bioaccumulated in Panicum antidotale among aquatic plant species while (Cd, Cu, Fe, Mn, Pb, and Zn) in Lantana camara and Ageratum conyzoids among terrestrial plants species. It is evident that all these plants have potential to phytoremediate various inorganic pollutants and can act as bioindicators. The physicochemical characteristics revealed high biochemical oxygen demand (BOD) and nitrate (NO3) contents and low dissolved oxygen (DO) in water samples while the high content of phosphates in soil and sediment samples.
Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM2.5) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM2.5. A 10 μg/m3 increase in GWR smoke PM2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019–1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM2.5 exposure method: a 10 μg/m3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026–1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931–1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.
As the global human population continues to grow, so too does our impact on the environment. The ingenuity with which our species has harnessed natural resources to fulfill our needs is dazzling. Even as we tighten our grip on the environment, however, the escalating extent of anthropogenic actions destabilizes long-standing ecological balances (1, 2). The dangers of mining, refining, and fossil fuel consumption now extend beyond occupational or proximate risks to global climate change (3). Among a plethora of environmental problems, extreme climate events are intensifying (4, 5). Storms, droughts, and floods cause direct destruction, but also have pervasive repercussions on food security, infectious disease transmission, and economic stability that take their toll for many years. For example, within weeks of the catastrophic wind and flood damage from the 2016 Hurricane Matthew in Haiti, there was a dramatic surge in cholera, among other devastating repercussions (6, 7). In a world where 1% of the population possesses 50% of the wealth (8), those worst affected by extreme climatic events and the aftermath are also the least able to rebound.
Severe smoke haze from biomass burning is frequently observed in Northern Thailand during dry months of February–April. Sparsely located monitoring stations operated in this vast mountainous region could not provide sufficient particulate matter (PM) data for exposure risk assessment. Satellite aerosol optical thickness (AOT) data could be used, but their reliable relationship with ground-based PM data should be first established. This study aimed to improve the regression model between PM10 and Moderate Resolution Imaging Spectroradiometer AOT with consideration of synoptic patterns to better assess the exposure risk in the area. Among four synoptic patterns, each representing the totality of meteorology governing Northern Thailand on a given day, most severe haze days belonged to pattern 2 that featured conditions of clear sky, stagnant air, and high PM10 levels. AOT-24 h PM10 regression model for pattern 2 had coefficient of determination improved to 0.51 from 0.39 of combined case. Daily exposure maps to PM10 in most severe haze period of February–April 2007 were produced for Chiangmai, the largest and most populated province in Northern Thailand. Regression model for pattern 2 was used to convert 24 h PM10 ranges of modified risk scale to corresponding AOT ranges, and the mapping was done using spatially continuous AOT values. The highest exposure risk to PM10 was shown in urban populated areas. Larger numbers of forest fire hot spots and more calm winds were observed on the days of higher exposure risk. Early warning and adequate health care plan are necessary to reduce exposure risk to future haze episodes in the area.
Contrary to widespread assumptions, next-generation high (annual to multi-annual) and ultra-high (sub-annual) resolution analysis of an Alpine glacier reveals that true historical minimum natural levels of lead in the atmosphere occurred only once in the last ca. 2000 years. During the Black Death pandemic, demographic and economic collapse interrupted metal production and atmospheric lead dropped to undetectable levels. This finding challenges current government and industry understanding of pre-industrial lead pollution and its potential implications for human health of children and adults worldwide. Available technology and geographic location have limited previous ice core investigations. We provide new high- (discrete, inductively coupled mass spectrometry, ICP-MS) and ultra-high resolution (laser ablation inductively coupled mass spectrometry, LA-ICP-MS) records of atmospheric lead deposition extracted from the high Alpine glacier Colle Gnifetti, in the Swiss-Italian Alps. We show that, contrary to the conventional wisdom, low levels at or approaching natural background occurred only in a single four-year period in the ca. 2000 years documented in the new ice core, during the Black Death (ca. 1349-1353 C.E.), the most devastating pandemic in Eurasian history. Ultra-high chronological resolution allows for the first time detailed and decisive comparison of the new glaciochemical data with historical records. Historical evidence shows that mining activity ceased upwind of the core site from ca. 1349 to 1353, while concurrently on the glacier lead (Pb) concentrations—dated by layer counting confirmed by radiocarbon dating—dropped to levels below detection, an order of magnitude beneath figures deemed low in earlier studies. Previous assumptions about pre-industrial “natural” background lead levels in the atmosphere—and potential impacts on humans—have been misleading, with significant implications for current environmental, industrial, and public health policy, as well as for the history of human lead exposure. Trans-disciplinary application of this new technology opens the door to new approaches to the anthropogenic impact on past and present human health. This article is protected by copyright. All rights reserved.