Researchers are interested in developing techniques to monitor, manage and predict the risks of gases and particles emitted from cement factories, which have a direct and negative impact on human health. Deep learning (DL) is a critical component of data mining, which further involves statistics and prediction. In this study, we developed a deep learning prediction model called the Deep Pollutant Prediction Model (DPPM). The data used for DPPM are separated into two types: observed data from a pollution monitoring station of the Institute of Mental Health in Ahmedabad City, India coded as (GJ001), to validate the model and simulated data generated using the Gaussian Plume Model for the hypothetical receptor (Laylan District, Kirkuk, Iraq) to predict the pollution that emitted from Kirkuk Cement Plant 5 km apart from the study area. The findings indicated that the DPPM has high efficiency in both Allahabad and Laylan stations, with more closed results for the data in the Laylan station, which is based on the Gaussian equation simulated data. Since the highest loss function value in the Laylan is 0.0221 of the CaO parameter, while it is 4.466 of the AQI parameter for the Allahabad Station, and the smallest loss function value in the Laylan is equal to 0.0041of both Fe2O3 and MgO parameters, it corresponds to 0.038 of Xylene for the Allahabad station. The results of the study proved that data continuity and non-volatility produce excellent outcomes for DPPM.

Evidence for long-term changes in the soil composition of selected organic compounds, brought about by exchanges with the atmosphere, is briefly reviewed. In the case of some compounds - such as benzo(a)pyrene and octachlorodibenzo-p-dioxin, soils may be significant long-term environmental sinks for atmospherically-derived material. In other cases - such as phenanthrene and some of the lighter PCBs, de-gassing or volatilisation from soil back to the air can occur under certain conditions. Hence the soil may act as a "short-term" sink, and a potential source to atmosphere. Indeed, for some 'semi-volatile' compounds used in large quantities in the past - such as PCBs, soil outgassing may actually be an extremely important source to contemporary air. Furthermore, soil outgassing from areas of former high use may provide an important driving mechanism for continued "global cycling" of a range of semi-volatile organochlorine compounds.

Syringic acid, which is a typical methoxyphenol emitted from wood combustion, can provide heterogeneous reaction sites for gaseous active components, influencing the concentrations of trace gases and the compositions of syringic acid. The heterogeneous uptake of O₃ on syringic acid was investigated using a flow tube reactor under ambient pressure. The initial uptake coefficient (γᵢ) and the steady-state uptake coefficient (γₛₛ) of O₃ linearly increased with syringic acid mass (0–0.16 μg cm⁻²) and temperature (278–328 K), while they decreased with increasing the O₃ concentration and the O₂ content. The γᵢ was independent of relative humidity (20%–70%), whereas γₛₛ decreased with relative humidity (7%–70%). The compositional changes of syringic acid by the ozonization were analyzed by the Fourier transform infrared spectrometer (FT-IR) and the gas chromatography-mass spectrometry (GC-MS), confirming the generation of 2,6-dimethoxy-1,4-benzoquinone. In addition, compared to that of fresh syringic acid, the mass absorption efficiency of syringic acid aged by O₃ exhibited an increase in the range of 290–320 nm.

To understand the effect of reaction temperature on sulfur during hydrothermal carbonization (HTC) of sewage sludge (SS), seven group of temperature (180–300 °C) were chosen to investigate the distributions and evolution of sulfur-containing compounds in hydrochar and the liquid products. Elemental analysis, X-ray photoelectron spectroscopy (XPS), and X-Ray powder diffraction (XRD) were used to characterize the distribution of sulfur in hydrochar. The concentrations of sulfate ions and sulfide were determined in the liquid sample. The experimental results showed that as the temperature increased, the O/C ratio decreased because of the improved carbonization degree of SS. After hydrothermal carbonization, 90% of the sulfur in SS remained in hydrochar. As the temperature increased, the amount of sulfur in the liquid, mainly in the form of sulfate ions, tended to decrease. However, the experimental results for the gas phase were the opposite of the liquid phase.

We evaluated the short-term effect of mixtures of ambient air pollutants on respiratory and circulatory morbidity in four Colombian cities.Daily Emergency Department (ED) visit records for respiratory and circulatory selected diagnosis and daily concentrations for six criteria air pollutant were obtained in four of the five major cities in Colombia: Bucaramanga, Bogota, Cali, and Medellin during 2011–2014. Using conditional Poisson time series analysis with fixed effects, we assessed the effect of air pollutants on health outcomes using single-pollutant, two-pollutant and specific mixtures-of-pollutant models controlling for meteorology and time trends. The percentages of change in the rate of ED visits and their 95% confidence interval were estimated for the joint effect of pollutants.In single-pollutant models increases in gases concentrations were associated with increases in ED visits for respiratory and circulatory diseases. The two-pollutant models for respiratory diseases showed that the effect of NO₂ alone (% change 2.86 95% CI 1.87–3.85) is higher than the joint effect of any of its combinations except for its combination with SO₂ (% change 3.05 95%CI 1.04–5.05). The two-pollutant models for circulatory diseases showed synergistic effects between NO₂ and PM₂.₅ (% change 2.13 95%CI 0.001–4.26). Specific mixtures models showed that the mixture of “traffic-related pollutants” has the higher joint effect on circulatory morbidity and respiratory morbidity.The results show the dominant effect of NO₂ in air pollution mixtures on respiratory and circulatory morbidity, and the synergistic effect of NO₂ and SO₂ in air pollution mixtures on respiratory morbidity.

Organic aerosol (OA) are always the most abundant species in terms of relative proportion to PM₂.₅ concentration in Beijing, while in previous studies, poor link between carbonaceous particles and their gaseous precursors were established based on field observation results. Through this study, we provided a comprehensive analysis of critical carbonaceous species in the atmosphere. The concentrations, diurnal variations, conversions, and gas-particle partitioning (F-factor) of 8 carbonaceous species, carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), volatile organic compounds (VOCs), non-methane hydrocarbon (NMHC), organic carbon (OC), elemental carbon (EC), and water soluble organic compounds (WSOCs), in Beijing were analyzed synthetically. Carbonaceous gases (CO, CO₂, VOCs, and CH₄) and OC/EC ratios exhibited double-peak diurnal patterns with a pronounced midnight peak, especially in winter. High correlation between VOCs and OC during winter nighttime indicated that OC was formed from VOCs precursors via an unknown mechanism at relative humidity greater than 50% and 80%, thereby promoting WSOC formation in PM₁ and PM₂.₅ respectively. The established F-factor method was effective to describe gas-to-particle transformation of carbonaceous species and was a good indicator for haze events since high F-factors corresponded with enhanced PM₂.₅ level. Moreover, higher F-factors in winter indicated carbonaceous species were more likely to exist as particles in Beijing. These results can help gain a comprehensive understanding of carbon cycle and formation of secondary organic aerosols from gaseous precursors in the atmosphere.

To better understand the mechanism of PM₂.₅ explosive growth (EG), we conducted concurrent measurements of gaseous pollutants, PM₂.₅ and its chemical composition (inorganic ions, organic carbon, and element carbon) with a time resolution of 1 h in Shanghai in late autumn and winter from 2014 to 2017. In this study, the EG events, which are defined as the net increase in the mass concentration of PM₂.₅ by more than 100 μg m⁻³ within hours, are separately discussed for 3, 6, or 9 h. The number of EG events decreased from 19 cases in 2014 to 6 cases in 2017 and the corresponding PM₂.₅ concentration on average decreased from 183.6 μg m⁻³ to 128.8 μg m⁻³. Both regional transport and stagnant weather (windspeed < 2.0 m s⁻¹) could lead to EG events. The potential source contribution function (PSCF) shows that the major high-pollution region is in East China (including Zhejiang, Jiangsu, Shandong, and Anhui Province) and the North China Plain. The contribution of stagnant conditions to EG episode hours of 55% (198 h, 156.9 μg m⁻³) is higher than that of regional transport (45%, 230 h, 163.0 μg m⁻³). To study the impact of local emission, chemical characteristics and driving factors of EG were discussed under stagnant conditions. The major components contributing to PM₂.₅ are NO₃⁻ (17.9%), organics (14.1%), SO₄²⁻ (13.1%), and NH₄⁺ (13.1%). The driving factors of EG events are the secondary aerosol formation of sulfate and nitrate and primary emissions (vehicle emissions, fireworks, and biomass burning), but the secondary transformation contributes more to EG events. The formation of sulfate and nitrate is dominated by gas-phase oxidation and heterogeneous reactions, which are enhanced by a high relative humidity. The current study helps to understand the chemical mechanism of haze and provides a scientific basis for air pollution control in Shanghai.

PM₂.₅ is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM₂.₅. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM₂.₅ were collected at Tianjin, a megacity in northern China. The ambient PM₂.₅ dataset, source information, and gas-to-particle ratios (such as SO₂/PM₂.₅, CO/PM₂.₅, and NOx/PM₂.₅ ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM₂.₅ was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

The fast economic development of southwest China has resulted in significant increases in the concentrations of reactive nitrogen (Nr) in the atmosphere. In this study, an urban (Chengdu, CD), suburban (Shifang, SF) and agriculture (Yanting, YT) – dominated location in the Sichuan Province, southwest China, were selected to investigate the atmospheric composition of Nr, their concentrations and deposition rates. We measured Nr concentrations in precipitation (NH₄⁺, NO₃⁻ and organic N (DON)), the gas phase (NH₃ and NO₂), and the aerosol particles (PM₂.₅), and calculated their fluxes over a two year period (2014–2016). Total annual N deposition rates were 49.2, 44.7 and 19.8 kg N ha⁻¹ yr⁻¹ at CD, SF and YT, respectively. Ammonia concentrations were larger at the urban and suburban sites than the agricultural site (12.2, 14.9, and 4.9 μg N m⁻³ at CD, SF and YT, respectively). This is consistent with the multitude of larger sources of NH₃, including city garbage, livestock and traffic, in the urban and suburban areas. Monthly NO₂ concentrations were lower in warmer compared to the colder months, but seasonal differences were insignificant. Daily PM₂.₅ concentrations ranged from 7.7 to 236.0, 5.0–210.4 and 4.2–128.4 μg m⁻³ at CD, SF and YT, respectively, and showed significant correlations with fine particulate NH₄⁺ and NO₃⁻ concentrations. Ratios of reduced to oxidized N were in the range of 1.6–2.7. This implies that the control of reduced Nr especially in urban environments is needed to improve local air quality.

In August 2016, continuous measurements of volatile organic compounds (VOCs) and trace gases were conducted at an urban site in Wuhan. Four high-ozone (O3) days and twenty-seven non-high-O3 days were identified according to the China's National Standard Level II (∼100 ppbv). The occurrence of high-O3 days was accompanied by tropical cyclones. Much higher concentrations of VOCs and carbon monoxide (CO) were observed on the high-O3 days (p < 0.01). Model simulations revealed that vehicle exhausts were the dominant sources of VOCs, contributing 45.4 ± 5.2% and 37.3 ± 2.9% during high-O3 and non-high-O3 days, respectively. Both vehicle exhausts and stationary combustion made significantly larger contributions to O3 production on high-O3 days (p < 0.01). Analysis using a chemical transport model found that local photochemical formation accounted for 74.7 ± 5.8% of the daytime O3, around twice the regional transport (32.2 ± 5.4%), while the nighttime O3 was mainly attributable to regional transport (59.1 ± 9.9%). The local O3 formation was generally limited by VOCs in urban Wuhan. To effectively control O3 pollution, the reduction ratio of VOCs to NOx concentrations should not be lower than 0.73, and the most efficient O3 abatement could be achieved by reducing VOCs from vehicle exhausts. This study contributes to the worldwide database of O3-VOC-NOx sensitivity research. Its findings will be helpful in formulating and implementing emission control strategies for dealing with O3 pollution in Wuhan.