Organic fertilizers, such as digestates and manure, are increasingly applied in agricultural systems because of the benefits they provide in terms of plant nutrients and soil quality. However, there are few investigations of N₂O emissions following digestate application to agricultural soils using process-based models. In this study, we modified the UK-DNDC model to include digestate applications to soils by adding digestate properties to the model and considering the effect of organic fertilizer pH. Using the modified model, N₂O emissions were simulated from two organic fertilizers (digested food waste and livestock slurry) applied to three farms in the United Kingdom: one growing winter wheat at Wensum (WE) and two grasslands at Pwllpeiran (PW) and North Wyke (NW). The annual cumulative gross (i.e. not excluding control emission) N₂O emissions were calculated using MATLAB trapezoidal numerical integration. The relative errors of the modeled annual cumulative emissions to the measured emissions ranged from −5.4% to 48%. Two-factor models, including linear, exponential and hyperbola responses, correlating total N loading and soil clay content to calculations of N₂O emissions and N₂O emission factors (EFs) were developed for calculations of emission fluxes and EFs. The squares of the correlation coefficients of the measured and two-factor linear modeled emissions were 0.998 and 0.999 for digestate and slurry, respectively, and the corresponding squares of correlation coefficients of the EFs were 0.998 and 0.938. The two-factor linear model also predicted that the EFs increased linearly with decreasing clay content and the maximum EFs for digestate and slurry were 0.95 and 0.76% of total N applied, respectively. This demonstrates that the modified UK_DNDC is a good tool to simulate N₂O emission from digestate and slurry and to calculate UK EFs using TIER 3 methodology..

Process-based models have been widely used for predicting environmental fate of contaminants. Nevertheless, accurate modeling of pentachlorophenol (PCP) dissipation in soils at the millimeter-scale remains a challenge due to the scarcity of observation data and uncertainty associated with model assumptions and estimation of the model parameters. To provide quantitative analysis of PCP-dissipation at the anaerobic/aerobic interface of a rhizobox experiment, this study implemented Bayesian parameter estimation for a process-based reactive chemical transport model. The model considered the main transport and transformation processes of chemicals including diffusion, sorption and degradation. The contributions of the processes to PCP dissipation were apportioned both in space and time. Using the maximum-a-posteriori (MAP) estimation of parameters, our model fitted the experimental data better compared with the previous work. Our results indicated that the most reactive zone for PCP dissipation occurred in the layer of 0–2.4 mm where degradation in solid phase dominated the PCP dissipation, while upward diffusion was the main mechanism for the reduction of PCP concentration in deeper layer (2.4–4.8 mm). By considering the coupled reactive transport of PCP and Cl⁻, the average degrees of PCP dechlorination in each layer were estimated from corresponding total concentrations of PCP and Cl⁻. The degrees of PCP dechlorination in the ponding water and the top layer of soil profile were highest, while 2,3,4,5- TeCP and 3,4,5- TCP were identified as the main dechlorination products in the soil. This study demonstrated that combining Bayesian estimation with process-based reactive chemical transport model can provide more insights of PCP dissipation at the millimeter-scale. This approach can help to understand complex dissipation mechanisms for other contaminants.

RT-qPCR allows sensitive detection of viral particles of both infectious and noninfectious viruses in water environments, but cannot discriminate non-infectious from infectious viruses. In this study, we aimed to optimize RT-qPCR-based detection of chlorine-inactivated human norovirus (NoV) and pepper mild mottle virus (PMMoV) in suspension by pretreatment with an optimal combination of a monoazide and a detergent that can efficiently penetrate damaged viral capsids. Four methods were compared to determine the efficacy of chlorine disinfection (at 1, 3, and 5 min mg/L): (A) RT-qPCR alone, (B) RT-qPCR assay preceded by magnetic bead separation for enrichment of viral particles (MBS-RT-qPCR), (C) MBS-RT-qPCR assay with pretreatment with propidium monoazide (PMA-MBS-RT-qPCR), and (D) PMA-MBS-RT-qPCR assay with pretreatment with sodium lauroyl sarcosinate (INCI-PMA-MBS-RT-qPCR). On the basis of a PMA optimization assay, 200 and 300 μM PMA were used in subsequent experiments for NoV GII.4 and PMMoV, respectively. Optimal INCI concentrations, having minimal influence on NoV GII.4 and PMMoV, were found to be 0.5% and 0.2% INCI, respectively. For NoV GII.4, there were significant differences (P < 0.05) in log₁₀ genome copies between the PMA-treated and the INCI + PMA-treated samples (log₁₀ genome copies differed by 1.11 and 0.59 log₁₀ for 3 and 5 min mg/L of chlorine, respectively). For PMMoV, INCI induced differences in log₁₀ genome copies of 0.92, 1.18, and 1.86, for 1, 3, and 5 min mg/L of chlorine, respectively. Overall, the results of this study indicate that an optimal combination of PMA and INCI could be very useful for evaluating disinfection methods in water treatment strategies.

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM₂.₅) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM₂.₅ concentrations during June 2015–May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM₂.₅ concentration was ∼30 μg m⁻³ (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m⁻³) during 2–5% of observation days. Average concentrations were ∼25 μg m⁻³ higher during winter than during monsoon and ∼8 μg m⁻³ higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM₂.₅ concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM₂.₅ in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

Despite not being used for decades in most countries, DDT remains ubiquitous in soils due to its persistence and intense past usage. Because of this it is still a pollutant of high global concern. Assessing long term dissipation of DDT from this reservoir is fundamental to understand future environmental and human exposure. Despite a large research effort, key properties controlling fate in soil (in particular, the degradation half-life (τₛₒᵢₗ)) are far from being fully quantified. This paper describes a case study in a large central European catchment where hundreds of measurements of p,p’-DDT concentrations in air, soil, river water and sediment are available for the last two decades. The goal was to deliver an integrated estimation of τₛₒᵢₗ by constraining a state-of-the-art hydrobiogeochemical-multimedia fate model of the catchment against the full body of empirical data available for this area. The INCA-Contaminants model was used for this scope. Good predictive performance against an (external) dataset of water and sediment concentrations was achieved with partitioning properties taken from the literature and τₛₒᵢₗ estimates obtained from forcing the model against empirical historical data of p,p’-DDT in the catchment multicompartments. This approach allowed estimation of p,p’-DDT degradation in soil after taking adequate consideration of losses due to runoff and volatilization. Estimated τₛₒᵢₗ ranged over 3000–3800 days. Degradation was the most important loss process, accounting on a yearly basis for more than 90% of the total dissipation. The total dissipation flux from the catchment soils was one order of magnitude higher than the total current atmospheric input estimated from atmospheric concentrations, suggesting that the bulk of p,p’-DDT currently being remobilized or lost is essentially that accumulated over two decades ago.

Heavy metals are concerned for its adverse effect on human health and long term burden on biogeochemical cycling in the ecosystem. In this study, a provincial-level emission inventory of 13 kinds of heavy metals including V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Sn, Sb, Ba and Pb from 10 anthropogenic sources was developed for China, based on the 2015 national emission inventory of primary particulate matters and source category-specific speciation profiles collected from 50 previous studies measured in China. Uncertainties associated with the speciation profiles were also evaluated. Our results suggested that total emissions of the 13 types of heavy metals in China are estimated at about 58000 ton for the year 2015. The iron production is the dominant source of heavy metal, contributing 42% of total emissions of heavy metals. The emissions of heavy metals vary significantly at regional scale, with largest amount of emissions concentrated in northern and eastern China. Particular, high emissions of Cr, Co, Ni, As and Sb (contributing 8%–18% of the national emissions) are found in Shandong where has large capacity of industrial production. Uncertainty analysis suggested that the implementation of province-specific source profiles in this study significantly reduced the emission uncertainties from (−89%, 289%) to (−99%, 91%), particularly for coal combustion. However, source profiles for industry sectors such as non-metallic mineral manufacturing are quite limited, resulting in a relative high uncertainty. The high-resolution emission inventories of heavy metals are essential not only for their distribution, deposition and transport studies, but for the design of policies to redress critical atmospheric environmental hazards at local and regional scales. Detailed investigation on source-specific profile in China are still needed to achieve more accurate estimations of heavy metals in the future.

Diesel vehicle emissions are the major source of genotoxic compounds in ambient air from urban areas. These pollutants are linked to risks of cardiovascular diseases, lung cancer, respiratory infections and adverse neurological effects. Biological events associated with exposure to some air pollutants are widely unknown but applying omics techniques may help to identify the molecular processes that link exposure to disease risk. Most data on health risks are related to long-term exposure, so the aim of this study is to investigate the impact of short-term exposure (two hours) to air pollutants on the blood transcriptome and microRNA expression levels.We analyzed transcriptomics and microRNA expression using microarray technology on blood samples from volunteers participating in studies in London, the Oxford Street cohort, and, in Barcelona, the TAPAS cohort. Personal exposure levels measurements of particulate matter (PM₁₀, PM₂.₅), ultrafine particles (UFPC), nitrogen oxides (NO₂, NO and NOx), black carbon (BC) and carbon oxides (CO and CO₂) were registered for each volunteer. Associations between air pollutant levels and gene/microRNA expression were evaluated using multivariate normal models (MVN).MVN-models identified compound-specific expression of blood cell genes and microRNAs associated with air pollution despite the low exposure levels, the short exposure periods and the relatively small-sized cohorts. Hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p and hsa-miR-92a-3p are found significantly expressed in association with exposures. These microRNAs target also relevant transcripts, indicating their potential relevance in the research of omics-biomarkers responding to air pollution. Furthermore, these microRNAs are also known to be associated with diseases previously linked to air pollution exposure including several cancers such lung cancer and Alzheimer's disease. In conclusion, we identified in this study promising compound-specific mRNA and microRNA biomarkers after two hours of exposure to low levels of air pollutants during two hours that suggest increased cancer risks.

Polybrominated diphenyl ethers (PBDEs) undergo debromination when they were exposed in zerovalent metal or bimetallic systems. Yet their debromination pathways and mechanisms in these systems were not well understood. Here we reported the debromination pathways of three BDE congeners (BDE-21, 25 and 29) by nano-zerovalent iron (n-ZVI). All these BDE congeners have three bromine substituents that were located in ortho-, meta- and para-positions. Results demonstrated that BDE-21, 25 and 29 preferentially debrominate meta-, ortho- and para-bromines, respectively, suggesting that bromine substituent at each position (i.e. ortho-, meta- or para-) of PBDEs can be preferentially removed. Singly occupied molecular orbitals of BDE anions are well correlated with their actual debromination pathways, which successfully explain why these BDE congeners exhibit certain debromination pathways in n-ZVI system. In addition, microscale zerovalent zinc (m-ZVZ), iron-based bimetals (Fe/Ag and Fe/Pd) were also used to debrominate PBDEs, with BDE-21 as target pollutant. We found that the debromination pathways of BDE-21 in m-ZVZ and Fe/Ag systems are the same to those in n-ZVI system, but were partially different from those in Fe/Pd systems. The debromination of BDE-21 in Pd-H2 system as well as the solvent kinetic isotope effect in single metal and bimetallic systems suggests that H atom transfer is the dominant mechanism in Fe/Pd system, while e-transfer is still the dominant mechanism in Fe/Ag system.

To better understand the precursor of disinfection by-products (DBPs) and provide useful information for water utilities to manage the drinking water, a study of DBP formation was conducted through chlorination of leaf organic matter (OM) from phoenix tree and algal OM from Chlorella vulgaris. DBPs investigated include trichloromethane (TCM), trichloroacetic acid (TCAA), dichloroacetic acid (DCAA), chloroacetic acid (CAA), dichloroacetonitrile (DCAN) and trichloroacetonitrile (TCNM). Results show that the specific yields (μg/mg C) of C-DBPs (TCM, CAA, DCAA and TCAA) from leaf OM were higher but the specific yields of N-DBPs (DCAN and TCNM) were lower than those from algal OM. Correlation analysis revealed that C-DBPs yields (μg/L) were significantly (p < 0.01) interrelated with each other (r = 0.937–0.996), and for each C-DBP, the hydrophobic OM contributed more to their formation (61–90% of total yields) as compared with hydrophilic OM. In spite of these characteristics, an in-depth examination was conducted revealing that the hydrophobicity and aromaticity of C-DBPs precursors were in the order of TCAA > DCAA & TCM > CAA. DCAN precursors were highly variable: they were dominated by hydrophobic OM (leaf OM: 86%) or hydrophilic OM (algal OM: 61%). Hydrophilic OM was the most important precursor for TCNM (76–79% of total yields), followed by hydrophobic neutral and base substances (29–45% of total yields), but the hydrophobic acids exhibited an inhibition role in TCNM formation.

In urbanized regions with expansive impervious surfaces and often low vegetation cover, air pollution due to motor vehicles and other combustion sources, is a problem. The poor air quality days in Montreal, Quebec are mainly due to fine particulate matter and ozone. Businesses using wood ovens are a source of particulates. Careful vegetation selection and increased green roof usage can improve air quality. This paper reviews different green roofs and the capability of plants in particulate matter (PM), ozone (O3) as well as nitrogen dioxide (NO2) level reductions. Both the recommended green roof category and plants to reduce these pollutants in Montreal's zone 5 hardiness region are provided. Green roofs with larger vegetation including shrubs and trees, or intensive green roofs, remove air pollutants to a greater extent and are advisable to implement on existing, retrofitted or new buildings. PM is most effectively captured by pines. The small Pinus strobus ‘Nana’, Pinus mugho var. pumilio, Pinus mugho ‘Slowmound’ and Pinus pumila ‘Dwarf Blue’ are good candidates for intensive green roofs. Drought tolerant, deciduous broadleaved trees with low biogenic volatile organic compound emissions including Japanese Maple or Acer palmatum ‘Shaina’ and ‘Mikawa-Yatsubusa’ are options to reduce O3 levels. Magnolias are tolerant to NO2 and it is important in their metabolic pathways. The small cold-tolerant Magnolia ‘Genie’ is a good option to remove NO2 in urban settings and to indirectly reduce O3 formation. Given the emissions by Montreal businesses' wood ovens, calculations performed based on their respective complex roof areas obtained via Google Earth Pro indicates 88% Pinus mugho var. pumilio roof coverage can annually remove 92.37 kg of PM10 of which 35.10 kg is PM2.5. The removal rates are 4.00 g/m2 and 1.52 g/m2 for PM10 and PM2.5, respectively. This paper provides insight to addressing air pollution through urban rooftop greening.