The arsenic concentration is an important issue in compost production. The main inputs of a compost factory, including kerbsides, green wastes, food industry wastes, and river weeds are investigated in this study. Also, this study investigated how treated timbers, ashes, and other contamination can impact arsenic concentration in compost production. The results showed that most treated timbers and all ashes of treated and untreated timbers contained significant amounts of arsenic. These results revealed that the presence of a small amount of treated timber ashes can significantly increase the arsenic concentration in composts. The results of the study show the arsenic concentration in compost increase during cold months, and it dropped during summer, which would be mostly because of high arsenic concentration in ashes of log burners. This study shows ashes of burning timbers can impact arsenic contamination mostly because of using Copper-Chrome-Arsenic wood preservatives (CCA). Also, the lab results show the arsenic level even in ashes of untreated timber is around 96 ppm. The ashes of H3, H4, and H5 treated timbers contain approximately 133,000, 155,000, and 179,000 ppm of arsenic, which one kg of them can increase arsenic concentration around 10 ppm in 13.3, 15.5 and 17.9 tons of dry compost products. The main problem is many people look at ashes and treated timber as organic materials; however, ashes of treated and untreated timbers contained high concentrations of arsenic. Therefore, it was necessary to warn people about the dangers of putting any ashes in organic waste bins.

Organobromine compounds comprise between 3 and 8% by weight of WEEE and mainly converted to HBr and Br₂ in the incinerator. However, these compounds, during the cooling of the flue gases, can form the PBDD/Fs in the post-combustion area of the furnace. Due to the many benefits of Oxy-combustion process, our group has developed a fluidised bed incinerator for burning the WEEE and plan to maximise HBr/Br₂ in the flue gas. Experimental results presented in the recent papers show that the combustion of the WEEE particles attains quickly to thermodynamic equilibrium. Thermodynamic modelling can, therefore, predict the concentration of brominated pollutants, particularly HBr, Br₂, HgBr₂, and Br˙ in the flue gas. In this paper, the effect of various parameters for increasing the HBr/Br₂ ratio in the flue gas has been investigated. The model shows that the addition of very small amounts of hydrogen in the post-combustion area can convert Br₂ and Br˙ into HBr.

Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2–50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ± 0.65 km) and wheat (AM: 2.34 ± 1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50–80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling.

Natural sources, such as soil and wind-erosion dust (SWD), biomass open burning (BOB), sea salt spray (SSAS) and biogenic source (BIO), are major contributors to atmospheric emissions of trace elements (TEs) globally. In this study, we used a comprehensive approach to account for area-, production- and biofuel consumption-based emission factor calculation methods, and thus developed an integrated high-resolution emission inventory for 15 types of TEs (As, B, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, V and Zn) originated from natural sources in China for the year 2015. The results show that national emissions of TEs in 2015 range from 7.45 tons (Hg) to 1, 400 tons (Zn) except for the extremely high emissions of Mn (10, 677 tons). SWD and BIO are identified as the top two source contributors, accounting for approximately 67.7% and 26.1% of the total emissions, respectively. Absolute emissions of TEs from natural sources are high in the Xinjiang, Inner Mongolia and Tibet autonomous regions with large areas of bare soil and desert. However, emission intensity of TEs per unit area in the Southern provinces of China is higher than those in Northern China and Southwestern China, with the Yunnan and Sichuan provinces displaying the highest emission intensity. Our results suggest that controlling SWD can play a significant role in reducing fugitive particulate matter and the associated emissions of TEs from natural sources in China; and desertification control is particularly critical in the Northwest provinces where the majority of deserts are located.

Two hundred sixty-three fine particulate matter (PM₂.₅) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM₂.₅ OC. Similar source categories were found from the results of the CMB and PMF models to PM₂.₅ OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.

In this study, nine congeners of polybrominated diphenyl ethers (PBDEs) and sixteen congeners of polycyclic aromatic hydrocarbons (PAHs) were measured in water samples to elucidate their spatial distribution, congener profiles, sources and ecological risks in the Guanlan River during both the dry season (DS) and the wet season (WS). The concentration of Σ9PBDE ranged from 58.40 to 186.35 ng/L with an average of 115.72 ng/L in the DS, and from 8.20 to 37.80 ng/L with an average of 22.15 ng/L in the WS. Meanwhile, the concentration of Σ16PAHs was ranged from 121.80 to 8371.70 ng/L with an average of 3271.18 ng/L in the DS and from 1.85 to 7124.25 ng/L with an average of 908.11 ng/L in the WS. The concentrations of PBDEs and PAHs in the DS were significantly higher than those in the WS, probably due to the dilution of the river during the rainy season. Moreover, the spatial distribution of pollutants revealed decreasing trend in the concentration from upstream to downstream and almost identical pattern was observed during both seasons. The source apportionment suggested that penta-BDE and to some extent octa-BDE commercial products were major sources of PBDEs in the study area. However, the sources of PAHs were mainly comprised of fossil fuels and biomass burning, followed by the petroleum products and their mixtures. The results of the ecological risk assessment indicated PBDEs contamination posed high ecological risks, while PAHs exhibited low or no ecological risks in the study area. Consistent with the environmental levels, the ecological risks of pollutants were relatively lower in the WS, compared to that in the DS. The results from this study would provide valuable baseline data and technical support for policy makers to protect the ecological environment of the Guanlan River.

A particulate matter (PM) source apportionment study was carried out in one of the most polluted districts of Tuscany (Italy), close to an old waste incinerator plant. Due to the high PM10 levels, an extensive field campaign was supported by the Regional Government to identify the main PM sources and quantify their contributions. PM10 daily samples were collected for one year and analysed by different techniques to obtain a complete chemical characterisation (elements, ions and carbon fractions). Hourly fine (<2.5 μm) and coarse (2.5–10 μm) aerosol samples were collected by a Streaker sampler for a shorter period and hourly elemental concentrations were obtained by PIXE.Positive Matrix Factorization (PMF) analysis of daily and hourly data allowed the identification of 10 main sources: six anthropogenic (Biomass Burning, Traffic, Secondary Nitrates, Secondary Sulphates, Incinerator, Heavy Oil combustion), two natural (Saharan Dust and Fresh Sea Salt) and two mixed sources (Local Dust and Aged Sea Salt). Biomass burning turned out to be the main source of PM, accounting for 30% of the PM10 mass as annual average, followed by Traffic (18%) and Secondary Nitrates (14%). Emissions from the Incinerator turned out to be only 2% of PM10 mass on average.PM10 composition and source apportionment have been assessed in a polluted area near a waste incinerator, by PMF analysis on daily and hourly compositional data sets.

This study characterizes impacts of peat-forest (PF) smoke on an urban environment through carbonaceous profiles of >260 daily PM₂.₅ samples collected during 2012, 2013 and 2015. Organic carbon (OC) and elemental carbon (EC) comprising eight carbonaceous fractions are examined for four sample groups – non-smoke-dominant (NSD), smoke-dominant (SD), episodic PM₂.₅ samples at the urban receptor, and near-source samples collected close to PF burning sites. PF smoke introduced much larger amounts of OC than EC, with OC accounting for up to 94% of total carbon (TC), or increasing by up to 20 times in receptor PM₂.₅. SD PM₂.₅ at the receptor site and near-source samples have OC3 and EC1 as the dominant fractions. Both sample classes also exhibit char-EC >1.4 times of soot-EC, characterizing smoldering-dominant PF smoke, unlike episodic PM₂.₅ at the receptor site featuring large amounts of pyrolyzed organic carbon (POC) and soot-EC. Relative to the mean NSD PM₂.₅ at the receptor, increasing strength of transboundary PF smoke enriches OC3 and OC4 fractions, on average, by factors of >3 for SD samples, and >14 for episodic samples. A peat-forest smoke (PFS) indicator, representing the concentration ratio of (OC2+OC3+POC) to soot-EC, shows a temporal trend satisfactorily correlating with an organic marker (levoglucosan) of biomass burning. The PFS indicator systematically differentiates influences of PF smoke from source to urban receptor sites, with a progressive mean of 3.6, 13.4 and 20.1 for NSD, SD and episodic samples respectively at the receptor site, and 54.7 for the near-source PM₂.₅. A PFS indicator of ≥5.0 is proposed to determine dominant influence of transboundary PF smoke on receptor urban PM₂.₅ in the equatorial Asia with ∼90% confidence. Assessing >2900 hourly OCEC data in 2017–2018 supports the applicability of the PFS indicator to evaluate hourly impacts of PF smoke on receptor urban PM₂.₅ in the Maritime Continent.

In China, the huge amounts of energy consumption caused severe carcinogenic polycyclic aromatic hydrocarbon (PAHs) concentration in the soil and ambient air. This paper summarized that the references published in 2008–2018 and suggested that biomass, coal and vehicular emissions were categorized as major sources of PAHs in China. In 2016, the emitted PAHs in China due to the incomplete combustion of fuel was about 32720 tonnes, and the contribution of the emission sources was the sequence: biomass combustion > residential coal combustion > vehicle > coke production > refine oil > power plant > natural gas combustion. The total amount of PAHs emission in China at 2016 was significantly decreased due to the decrease of the proportion of crop resides burning (indoor and open burning).The geographical distribution of PAHs concentration demonstrated that PAHs concentration in the urban soil is 0.092–4.733 μg/g. At 2008–2012, the serious PAHs concentration in the urban soil occurred in the eastern China, which was shifted to western China after 2012.The concentration of particulate and gaseous PAHs in China is 1–151 ng/m3 and 1.08–217 ng/m3, respectively. The concentration of particle-bound PAHs in the southwest and eastern region are lower than that in north and central region of China. The incremental lifetime cancer risk (ILCR) analysis demonstrates that ILCR in the soil and ambient air in China is below the acceptable cancer risk level of 10−6 recommended by US Environmental Protection Agency (EPA), which mean that there is a low potential PAHs carcinogenic risk for the soil and ambient air in China.