Since an invention of synthetic fibers (textiles), our life quality has been improved. However, the cumulative production and disposal of them have perceived as significant since they are not biodegradable and hard to be upcycled/recycled. From washing textiles, microplastics are released into the environment, which are regarded as emerging contaminants. As a means for source reduction of microplastics, this study proposed a rapid disposal platform for waste textiles (WTs), converting them into value-added products. To this end, catalytic pyrolysis of WT was studied. To offer more environmentally sound process, CO₂ was used as a raw material for WT pyrolysis. Thermal cracking of WT led to the production of syngas and CH₄ under the CO₂ environment. CO₂ resulted in additional CO production via gas phase reaction with volatile compounds evolved from pyrolysis of WT. To expedite the reaction kinetics for syngas formation, catalytic pyrolysis was done over Co-based catalyst. Comparing to non-catalytic pyrolysis, CO₂-assisted catalytic pyrolysis had 3- and 8-times higher production of H₂ and CO, respectively. This process also suppressed catalyst deactivation, converting more than 80 wt% of WT into syngas and CH₄. The more generation of CO from the use of CO₂ as a raw material offers an effective means to minimize the formations of harmful chemical species, such as benzene derivatives and polycyclic aromatic hydrocarbons.

Rapid economic growth, urban sprawl, and unplanned industrialization has increased socioeconomic statuses but also decreased air quality in South Asian developing countries. Therefore, severe increase in air pollution has been a threat of local population, regarding health statuses, livability and quality of life. It is necessary to estimate fine-scale spatiotemporal distribution of ambient PM₂.₅ in a national context so that the environmental planners and government officials can use it for environmental protocol development and policy-making. In this study, a spatiotemporal land use regression (LUR) model is developed to refine global air quality data to the national-scale ambient PM₂.₅ exposure in a high-density country in South Asia – Pakistan. Combining with transport network, patterns of land use, local meteorological conditions, geographic characteristics, landscape characteristics, and satellite-derived data, our resultant model explains 54.5% of the variation in ambient PM₂.₅ concentration level. Furthermore, tree coverage and road transport are identified to be two influential factors of the national-scale spatial variation of PM₂.₅ in Pakistan, which implied that urbanization might be the major cause of air pollution across the country. In conclusion, our resultant LUR model as well as the spatial map of ambient PM₂.₅ concentration level can be used as a supporting tool for national health risk management and environmental planning, and could also contribute to the air quality management and pollution reduction actions of Pakistan.

The increasing demand for clean water resources for human consumption, is raising concerning about the sustainable worldwide provisioning. In Mexico, rivers near to high-density urbanizations are subject to irrational exploitation where polluted water is a risk for human health. Therefore, the aims of this study are to analyze water quality parameters and bacterial community dynamics to understand the relation between them, in the Apatlaco river, which presents a clear environmental perturbance. Parameters such as total coliforms, chemical oxygen demand, harness, ammonium, nitrite, nitrate, total Kjeldahl nitrogen, dissolved oxygen, total phosphorus, total dissolved solids, and temperature were analyzed in 17 sampling points along the river. The high pollution level was registered in the sampling point 10 with 480 mg/L chemical oxygen demand, 7 mg/L nitrite, 34 mg/L nitrate, 2 mg/L dissolved oxygen, and 299 mg/L of total dissolved solids. From these sites, we selected four samples for DNA extraction and performed a metagenomic analysis using a whole metagenome shotgun approach, to compare the microbial communities between polluted and non-polluted sites. In general, Proteobacteria was the most representative phylum in all sites. However, the clean water reference point was enriched with microorganism from the Limnohabitans genus, a planktonic bacterium widespread in freshwater ecosystems. Nevertheless, in the polluted sampled sites, we found a high abundance of potential opportunistic pathogen genera such as Acinetobacter, Arcobacter, and Myroides, among others. This suggests that in addition to water contamination, an imminent human health risk due to pathogenic bacteria can potentially affect a population of ∼1.6 million people dwelling nearby. These results will contribute to the knowledge regarding anthropogenic pollution on the microbial population dynamic and how they affect human health and life quality.

Ecosystem services are present everywhere, green vegetation coverage (or green areas) is one of the primary sources of ecosystem services considering urban areas sustainability and peoples urban life quality. Urban vegetation cover loss decreases the capacity of nature to provision ecosystem services; the loss of urban vegetation is also observed within the Amazon. This study aims at identifying urban vegetation loss and relate it to the provision of ecosystem services of reduction of air quality, reduction of air pollution, and climate regulation. Urban vegetation coverage loss was calculated using NDVI on LANDSAT 5 imagery over a 23-year period from 1986 to 2009. NDVI thresholds were arbitrarily selected, and complemented by in locus observation, to establish guidelines for quantitative (area) and qualitative (density) evolution of green cover, divided in six different categories, named as water, bare soil, poor vegetation, moderate vegetation, dense vegetation and very dense vegetation. Data on air pollution, noise pollution and temperature were outsourced from previous works. Measurement show a significant loss of very dense, dense and moderate vegetation coverage and an increase in poor vegetation and bare soil areas, in accordance to increase in air and noise pollution, and local temperature, and provides positive refashions between the loss of urban green coverage and decrease in ecosystem services.

PM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a ‘high-value increasing and low-value decreasing’ trend. Areas with annual PM2.5 concentrations of more than 70μg/m3 and less than 10μg/m3 expanded, while areas with of an annual PM2.5 concentrations of 10–25μg/m3 shrank. The most heavily PM2.5-polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5-polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5-polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5-polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks.

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management.

The purpose of this paper is to integrate the concepts of ecosystem services and disservices when assessing the efficacy of using urban forests for mitigating pollution. A brief review of the literature identifies some pollution mitigation ecosystem services provided by urban forests. Existing ecosystem services definitions and typologies from the economics and ecological literature are adapted and applied to urban forest management and the concepts of ecosystem disservices from natural and semi-natural systems are discussed. Examples of the urban forest ecosystem services of air quality and carbon dioxide sequestration are used to illustrate issues associated with assessing their efficacy in mitigating urban pollution. Development of urban forest management alternatives that mitigate pollution should consider scale, contexts, heterogeneity, management intensities and other social and economic co-benefits, tradeoffs, and costs affecting stakeholders and urban sustainability goals.

Particulate matter with an aerodynamic equivalent diameter of 2.5 μm or less in ambient air (PM2.5) has become a global public and environmental problem, and the control of the PM2.5 concentration in air is an urgent problem. PM2.5 can easily penetrate the skin, activating the inflammatory response in skin, unbalancing the skin barrier function, and inducing skin aging. Hyperpigmentation is the main manifestation of skin aging and has a considerable impact on quality of life worldwide. To date, no research on the influence of PM2.5 on hyperpigmentation has been conducted. Here, we illustrate that PM2.5 can induce melanogenesis in vivo and in vitro by regulating TYR, TYRP1, TYRP2, and MITF expression via AhR/MAPK signaling activation. Furthermore, PM2.5 increased α-MSH paracrine levels, which in turn promote hyperpigmentation. Our results provide a deeper understanding of how PM2.5 disrupts skin homeostasis and function. Treatment with AhR antagonists may be a potential therapeutic strategy for hyperpigmentation induced by PM2.5.

Ontario is Canada’s provincial leader in wind energy, with over 4000 MW of installed capacity supplying approximately five percent of the province’s electricity demand. Wind energy is now one of the fastest-growing sources of renewable power in Canada and many other countries. However, its possible negative impact on population health, as a new source of environmental noise, has raised concerns for people living in proximity to wind turbines (WTs). The aims of this study were to assess the effect of individual differences and annoyance on the self-reported general health and health-related quality of life (QOL) of nearby residents, using a pre- and post-exposure design. Prospective cohort data were collected before and after WT operations, from the individuals (n = 43) in Ontario, Canada. General health and QOL metrics were measured using standard scales, such as SF12, life satisfaction scales developed by Diener (SWLS) and the Canadian Community Health Survey (CCHS-SWL). The mean values for the Mental Component Score of SF12 (p = 0.002), SWLS (p < 0.001), and CCHS-SWL (p = 0.044) significantly worsened after WT operation for those participants who had a negative attitude to WTs, who voiced concerns about property devaluation, and/or who reported being visually or noise annoyed.

Understanding of cadmium (Cd) uptake mechanism and development of lower Cd crop genotypes are crucial for combating its phytotoxicity and meeting 70% increase in food demand by 2050. Bio-accumulation of Cd continuously challenges quality of life specifically in regions without adequate environmental planning. Here, we investigated the mechanisms operating in Cd tolerance of two rice genotypes (Heizhan-43 and Yinni-801). Damage to chlorophyll contents and PSII, histochemical staining and quantification of reactive oxygen species (ROS), cell viability and osmolyte accumulation were studied to decipher the interactions between Cd and zinc (Zn) by applying two Cd and two Zn levels (alone as well as combined). Cd²⁺ and Ca²⁺ fluxes were also measured by employing sole Cd₁₀₀ (100 μmol L⁻¹) and Zn₅₀ (50 μmol L⁻¹), and their combination with microelectrode ion flux estimation (MIFE) technique. Cd toxicity substantially reduced chlorophyll contents and maximal photochemical efficiency (Fᵥ/Fₘ) compared to control plants. Zn supplementation reverted the Cd-induced toxicity by augmenting osmoprotectants and interfering with ROS homeostasis under combined treatments, particularly in Yinni-801 genotype. Fluorescence microscopy indicated a unique pattern of live and dead root cells, depicting more damage with Cd₁₀, Cd₁₅ and Cd₁₅+Zn₅₀. Our results confer that Cd²⁺ impairs the uptake of Ca²⁺ whereas, Zn not only competes with Cd²⁺ but also Ca²⁺, thereby modifying ion homeostasis in rice plants. This study suggests that exogenous application of Zn is beneficial for rice plants in ameliorating Cd toxicity in a genotype and dose dependent manner by minimizing ROS generation and suppressing collective oxidative damage. The observations confer that Yinni-801 performed better than Heizhan-43 genotype mainly under combined Zn treatments with low-Cd, presenting Zn fortification as a solution to increase rice production.