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.

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.

The rate of deforestation in Brazil increased by 29% between 2015 and 2016, resulting in an increase of greenhouse gas emissions (GHG) of 9%. Deforestation fires in the Amazonia are the main source of GHG in Brazil. In this work, amounts of CO2, CO, main hydrocarbon gases and PM2.5 emitted during deforestation fires, under real conditions directly in Brazilian Amazonia, were determined. A brief discussion of the relationship between the annual emission of CO2 equivalent (CO2,eq) and Paris Agreement was conducted. Experimental fires were carried out in Western Amazonia (Candeias do Jamari, Rio Branco and Cruzeiro do Sul) and results were compared with a previous fire carried out in Eastern Amazonia (Alta Floresta). The average total fresh biomass on the ground before burning and the total biomass consumption were estimated to be 591 ton ha−1 and 33%, respectively. CO2, CO, CH4, and non–methane hydrocarbon (NMHC) average emission factors, for the four sites, were 1568, 140, 8, and 3 g kg−1 of burned dry biomass, respectively. PM2.5 showed large variation among the sites (0.9–16 g kg−1). Emissions per hectare of forest were estimated as 216,696 kg of CO2, 18,979 kg of CO, 1,058 kg of CH4, and 496 kg of NMHC. The average annual emission of equivalent CO2 was estimated as 301 ± 53 Mt year−1 for the Brazilian Amazonia forest. From 2013, the estimated CO2,eq showed a trend to increase in Amazon region. The present study is an alert and provides important information that can be used in the development of the public policies to control emissions and deforestation in the Brazilian Amazonia.

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.

Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context.For the Chino dairies, MISTIR-AMOG ammonia (NH₃) agreement was within 5% (15.7 versus 14.9 Gg yr⁻¹, respectively) using all information. Methane (CH₄) emissions were 30 Gg yr⁻¹ for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported.Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH₄, NH₃, and CO₂ emissions were 91, 209, and 8200 Mg yr⁻¹, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion.NH₃ downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

In Mediterranean areas, dry deposition is a major component of the total atmospheric N input to natural habitats, particularly to forest ecosystems. An innovative approach, combining the empirical inferential method (EIM) for surface deposition of NO₃⁻ and NH₄⁺ with stomatal uptake of NH₃, HNO₃ and NO₂ derived from the DO₃SE (Deposition of Ozone and Stomatal Exchange) model, was used to estimate total dry deposition of inorganic N air pollutants in four holm oak forests under Mediterranean conditions in Spain. The estimated total deposition varied among the sites and matched the geographical patterns previously found in model estimates: higher deposition was determined at the northern site (28.9 kg N ha⁻¹ year⁻¹) and at the northeastern sites (17.8 and 12.5 kg N ha⁻¹ year⁻¹) than at the central-Spain site (9.4 kg N ha⁻¹ year⁻¹). On average, the estimated dry deposition of atmospheric N represented 77% ± 2% of the total deposition of N, of which surface deposition of gaseous and particulate atmospheric N averaged 10.0 ± 2.9 kg N ha⁻¹ year⁻¹ for the four sites (58% of the total deposition), and stomatal deposition of N gases averaged 3.3 ± 0.8 kg N ha⁻¹ year⁻¹ (19% of the total deposition). Deposition of atmospheric inorganic N was dominated by the surface deposition of oxidized N in all the forests (means of 54% and 42% of the dry and total deposition, respectively). The relative contribution of NO₂ to dry deposition averaged from 19% in the peri-urban forests to 11% in the most natural site. During the monitoring period, the empirical critical loads provisionally proposed for ecosystem protection (10–20 kg N ha⁻¹ year⁻¹) was exceeded in three of the four studied forests.

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.

Faced with the unavoidable reality of the emission of pollutant gases by vessels both while sailing and when performing in-port manoeuvres, the international community has devised an extensive set of rules to limit greenhouse gas emissions and the emission of other pollutants which are bad for our health. In order to make these reductions in the emissions, the areas addressed are the engine regime or speed control, the quality of the fuel used, the state of conservation of the vessel and its hull or the time taken to perform the manoeuvres of mooring and unmooring. One factor which is having a strong influence on this last aspect is the installation in commercial ports of Automatic Mooring Systems using suction cups (AMS). These devices, which help to reduce considerably the time required to perform the mooring and unmooring manoeuvres, allow the times taken in operations for making steady a vessel to land and of releasing it to sail away to be reduced from some tens of minutes to a few seconds. The aim of this work is to verify the effect of the AMS on the emission of pollutant gases in the surroundings of the installations devoted to Ro-Ro/Pax vessel traffic. In particular, will focus on the CO2 emissions produced by vessels during mooring operations using two different calculation methodologies (EPA and ENTEC), first when using traditional mooring methods as a means of securing the vessel to the dock and second when using only the AMS, to finally carry out a comparison of the results. Will conclude with a discussion on the values of the reduction in emissions obtained and the advantages of installing AMS in commercial ports. In the RoRo/Pax terminals in which the AMS is installed and operating, a reduction in CO2 emissions of 97% has been estimated.

In the Mediterranean ecosystem, wildfires are very frequent and the predicted future with a probable increase of fires could drastically modify the vegetation scenarios. Vegetation fires are an important source of gases and primary emissions of fine carbonaceous particles in the atmosphere. In this paper, we present gaseous and particulate emissions data from the combustion of different plant tissues (needles/leaves, branches and needle/leaf litter), obtained from one conifer (Pinus halepensis) and one deciduous broadleaf tree (Quercus pubescens). Both species are commonly found throughout the Mediterranean area, often subject to wildfires. Experiments were carried out in a combustion chamber continuously sampling emissions throughout the different phases of a fire (pre-ignition, flaming and smoldering). We identified and quantified 83 volatile organic compounds including important carcinogens that can affect human health. CO and CO₂ were the main gaseous species emitted, benzene and toluene were the dominant aromatic hydrocarbons, methyl-vinyl-ketone and methyl-ethyl-ketone were the most abundant measured oxygenated volatile organic compounds. CO₂ and methane emissions peaked during the flaming phase, while the peak of CO emissions occurred during the smoldering phase. Overall, needle/leaf combustion released a greater amount of volatile organic compounds into the atmosphere than the combustion of branches and litter. There were few differences between emissions from the combustion of the two tree species, except for some compounds. The combustion of P. halepensis released a great amount of monoterpenes as α-pinene, β-pinene, p-cymene, sabinene, 3-carene, terpinolene and camphene that are not emitted from the combustion of Q. pubescens. The combustion of branches showed the longest duration of flaming and peak of temperature. Data presented appear crucial for modeling with the intent of understanding the loss of C during different phases of fire and how different typologies of biomass can affect wildfires and their speciation emissions profile.