Super capacity of ligand-engineered biochar for sorption of malachite green dye: key role of functional moieties and mesoporous structure.

This study synthesized a new thiomalic acid-modified rice husk biochar (TMA-BC) as a versatile and eco-friendly sorbent. After undergoing chemical treatments, the mercerized rice husk biochar (NaOH-BC) and TMA-BC samples showed higher BET surface area values of 277.1 m2/g and 305.8 m2/g, respectively, compared to the pristine biochar (BC) sample, which had a surface area of 234.2 m2/g. In batch adsorption experiments, it was found that the highest removal efficiency for malachite green (MG) was achieved with TMA-BC, reaching 96.4%, while NaOH-BC and BC exhibited removal efficiencies of 38.6% and 27.9%, respectively, at pH 8. The engineered TMA-BC exhibited a super adsorption capacity of 104.17 mg/g for MG dye at pH 8.0 and 25 °C with a dosage of 2 g/L. The SEM, TEM, XPS, and FTIR spectroscopy analyses were performed to examine mesoporous features and successful TMA-BC carboxylic and thiol functional groups grafting on biochar. Electrostatic forces, such as π - π interactions, hydrogen bonding, and pore intrusion, were identified as key factors in the sorption of MG dye. As compared to single-solution adsorption experiments, the binary solution experiments performed at optimized dosages of undesired ions, such as humic acid, sodium dodecyl sulfate surfactant, NaCl, and NaSCN, reflected an increase in MG dye removal of 2.8%, 8.7%, 5.4%, and 12.7%, respectively, which was attributed to unique mesoporous features and grafted functional groups of TMA-BC. Furthermore, the TMA-BC showed promising reusability up to three cycles. Our study indicates that mediocre biochar modified with TMA can provide an eco-friendly and cost-effective alternative to commercially accessible adsorbents.

Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach.

Over the past few years, surface ozone (O3) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 µg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.

Evaluating the relationship between groundwater quality and land use in an urbanized watershed.

Understanding the impact of urbanization on groundwater quality is critical. Effective water management requires understanding the relationship between land use and water quality. The study's goals were to compare the effects of land use, identify the types of land that impact hydrochemistry, and define how different land use affects water quality. For this purpose, the comparative relationship between groundwater quality, land use classes and landscape metrics were established for the years 2016 and 2021. Water samples were collected from 42 wells, and different hydro-chemical variables were considered to calculate the water quality index (WQI). The WQI value in 2016 ranged from 26.49 to 151.03 and 29.65 to 155.62 in 2021. The results indicate that the water quality in most parts of the study area is moderate for drinking and domestic purpose use. The google earth engine platform was used and radiometrically corrected and orthorectified Sentinel-2 satellite images were processed to classify land use classes for selected years. Five buffer zones were established within a 2-km watershed along each well site, and the effects of land use types and landscape metrics on water quality in the buffer zones were analyzed. Results revealed that the effects of land use types on water quality were mainly reflected in buffer 1 (B1), buffer 4 (B4), buffer 5 (B5) in 2016 and B1, buffer 3 (B3), and B5 in 2021. The impacts of landscape-level metrics on water quality are mainly reflected in buffer 2 (B2) and B3 in 2021, while at the class-level, they are mainly reflected in B1 and B4 in 2021. The redundancy analysis revealed that different hydro-chemical variables behaved differently with the land use classes and landscape metrics in the various buffer zones.

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly.

Air is a diverse mixture of gaseous and suspended solid particles. Several new substances are being added to the air daily, polluting it and causing human health effects. Particulate matter (PM) is the primary health concern among these air toxins. The World Health Organization (WHO) addressed the fact that particulate pollution affects human health more severely than other air pollutants. The spread of air pollution and viruses, two of our millennium's most serious concerns, have been linked closely. Coronavirus disease 2019 (COVID-19) can spread through the air, and PM could act as a host to spread the virus beyond those in close contact. Studies on COVID-19 cover diverse environmental segments and become complicated with time. As PM pollution is related to everyday life, an essential awareness regarding PM-impacted COVID-19 among the masses is required, which can help researchers understand the various features of ambient particulate pollution, particularly in the era of COVID-19. Given this, the present work provides an overview of the recent developments in COVID-19 research linked to ambient particulate studies. This review summarizes the effect of the lockdown on the characteristics of ambient particulate matter pollution, the transmission mechanism of COVID-19, and the combined health repercussions of PM pollution. In addition to a comprehensive evaluation of the implementation of the lockdown, its rationales-based on topographic and socioeconomic dynamics-are also discussed in detail. The current review is expected to encourage and motivate academics to concentrate on improving air quality management and COVID-19 control.

Why is inhalation the most discriminative route of microplastics exposure?

Recent research suggests a definite distinction between indoor and outdoor microplastics (MPs). However, knowledge of different MP kinds and relative exposure via inhalation to humans in outdoor and indoor locations is lacking. Notably, MPs formed from various plastic types could have distinct features, and the relative health risk varies by environment. For example, outdoor polyethylene (PE) goods have recently become more popular. These products are generally of poor structure and recycled material, making them more susceptible to decay. Particularly in the outdoor environment, the constant exposure to an open-air environment increases the risk of fragmentation and atmospheric mixing and thus facilitates MP's availability. Using PE as an example, we aimed to emphasize the importance of explicitly defining exposure intensity and the health risk of each MP type, especially in contrasting situations such as indoor and outdoor. Unchecked and excessive use of these materials can be hazardous, whereas lowering or replacing PE with alternative plastics can significantly reduce potential health hazards.

Construct social-behavioral association network to study management impact on waterbirds community ecology using digital video recording cameras.

Studying social-behavior and species associations in ecological communities is challenging because it is difficult to observe the interactions in the field. Animal behavior is especially difficult to observe when selection of habitat and activities are linked to energy costs of long-distance movement. Migrating communities tend to be resource specific and prefer environments that offer more suitability for coexisting in a shared space and time. Given the recent advances in digital technologies, digital video recording systems are gaining popularity in wildlife research and management. We used digital video recording cameras to study social interactions and species-habitat linkages for wintering waterbirds communities in shared habitats. Examining over 8,640 hr of video footages, we built tetrapartite social-behavioral association network of wintering waterbirds over habitat (n = 5) selection events in sites with distinct management regimes. We analyzed these networks to identify hub species and species role in activity persistence, and to explore the effects of hydrological regime on these network characteristics. Although the differences in network attributes were not significant at treatment level (p = .297) in terms of network composition and keystone species composition, our results indicated that network attributes were significantly different (p = .000, r 2 = .278) at habitat level. There were evidences suggesting that the habitat quality was better at the managed sites, where the formed networks had more species, more network nodes and edges, higher edge density, and stronger intra- and inter-species interactions. In addition, we also calculated the species interaction preference scores (SIPS) and behavioral interaction preference scores (BIPS) of each network. The results showed that species synchronize activities in shared space for temporal niche partitioning in order to avoid or minimize any potential competition for shared space. Our social network analysis (SNA) approach is likely to provide a practical use for ecosystem management and biodiversity conservation.

Application of biochar in advanced oxidation processes: supportive, adsorptive, and catalytic role.

The advanced oxidation processes (AOPs), especially sulphate radical (SO4•-)-based AOPs (SR-AOPs), have been considered more effective, selective, and prominent technologies for the removal of highly toxic emerging contaminants (ECs) due to wide operational pH range and relatively higher oxidation potential (2.5-3.1 V). Recently, biochar (BC)-based composite materials have been introduced in AOPs due to the dual benefits of adsorption and catalytic degradation, but the scientific review of BC-based catalysts for the generation of reactive oxygen species (ROSs) through radical- and non-radical-oriented routes for EC removal was rarely reported. The chemical treatments, such as acid/base treatment, chemical oxidation, surfactant incorporation, and coating and impregnation of minerals, were applied to make BC suitable as supporting materials (SMs) for the loading of Fenton catalysts to boost up peroxymonosulphate/persulphate/H2O2 activation to get ROSs including •OH, SO4•-, 1O2, and O2•- for targeted pollutant degradation. In this review, all the possible merits of BC-based catalysts including supportive, adsorptive, and catalytic role are summarised along with the possible route for the development prospects of BC properties. The limitations of SR-AOPs especially on production of non-desired oxyanions, as well as disinfection intermediates and their potential solutions, have been identified. Lastly, the knowledge gap and future-oriented research needs are highlighted.

A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources.

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 ±â€¯27.1 µg/m3 (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 ±â€¯13.9 µg/m3 (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.

Using big data from air quality monitors to evaluate indoor PM2.5 exposure in buildings: Case study in Beijing.

Due to time- and expense- consuming of conventional indoor PM2.5 (particulate matter with aerodynamic diameter of less than 2.5 µm) sampling, the sample size in previous studies was generally small, which leaded to high heterogeneity in indoor PM2.5 exposure assessment. Based on 4403 indoor air monitors in Beijing, this study evaluated indoor PM2.5 exposure from 15th March 2016 to 14th March 2017. Indoor PM2.5 concentration in Beijing was estimated to be 38.6 ±â€¯18.4 µg/m3. Specifically, the concentration in non-heating season was 34.9 ±â€¯15.8 µg/m3, which was 24% lower than that in heating season (46.1 ±â€¯21.2 µg/m3). A significant correlation between indoor and ambient PM2.5 (p < 0.05) was evident with an infiltration factor of 0.21, and the ambient PM2.5 contributed approximately 52% and 42% to indoor PM2.5 for non-heating and heating seasons, respectively. Meanwhile, the mean indoor/outdoor (I/O) ratio was estimated to be 0.73 ±â€¯0.54. Finally, the adjusted PM2.5 exposure level integrating the indoor and outdoor impact was calculated to be 46.8 ±â€¯27.4 µg/m3, which was approximately 42% lower than estimation only relied on ambient PM2.5 concentration. This study is the first attempt to employ big data from commercial air monitors to evaluate indoor PM2.5 exposure and risk in Beijing, which may be instrumental to indoor PM2.5 pollution control.