Sewage sludge poses a serious threat to the environment as the world struggles to keep up with its rapid generation. Biological waste treatment technologies such as composting and vermicomposting are widely regarded as clean and sustainable methods to manage sewage sludge. The aim of this study was to evaluate the carbon dioxide and methane emissions during composting and vermicomposting of sewage sludge under the effect of different proportions of additive straw pellets. Composting and vermicomposting with Eisenia Andrei treatments were conducted over a 60 days lasting period, taking sewage sludge as the processing object and using pelletized wheat straw as the additional substrate. Four treatments were set up (T1) 100% sewage sludge, (T2) 75% sewage sludge + 25% pelletized wheat straw,(T3) 50% sewage sludge + 50% pelletized wheat straw, (T4) 25% sewage sludge + 75% pelletized wheat straw. The percentage is presented as the weight ratio of both substrates for all treatments. All the treatments were transferred to fermenter barrels for composting and also, the same treatments used in composting were transferred to worm-bins for vermicomposting. Methane and carbon dioxide concentration in the gas phase released from treatments were daily measured. The results indicated that both composting and vermicomposting produce a significant (p≤0.001) amount of methane and carbon dioxide emissions from all treatments. The results showed that vermicomposting significantly reduced methane emissions by 38%, 34%, and 18% for treatments contains 25%, 50%, and 75% straw pellets respectively compared to composting. The same proportions of pelletized wheat straw used in composting increased carbon dioxide emissions during vermicomposting by 75%, 64%, and 89%. In conclusion, vermicomposting is effective at reducing greenhouse gas emissions from composting. Therefore, from this finding, vermicomposting could represent an option for reducing gas emissions particularly the emission of methane which is radiatively stronger than carbon dioxide.

Acknowledgment: Financial support for this work was provided by the Ministry of Agriculture of the Czech Republic under the NAZV project number QK1910095.

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge with months in advance of the precipitation regime, which would allow a proper management of water resources. In this work, a series of 6 experiments was made with mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15 months forecast each of monthly cumulative precipitation starting at two dates for three years with different meteorological characteristics, one dry year (2004), one year that started dry and turned rainy (2005) and one year signaled by the occurrence of several tropical storms (2008). ERA-Interim reanalysis data were used for initial and border conditions and runs started one month before the beginnings of the rainy and the dry seasons respectively. In a general sense, the experience of using WRF indicates that it is a valid resource for seasonal forecast, since results obtained are in the same range as those reported by literature for similar cases. Several limitations were revealed by the results, such as that forecasts underestimate the monthly cumulative precipitation figures, tropical storms entering through the borders may follow courses different from the real ones inside the working domain, storms that developed inside the domain were not reproduced by WRF and differences in initial conditions led to significantly different forecasts for corresponding time steps (non linearity). It is recommended to carry on ensemble forecast experiments changing model parameterizations and initial conditions.

A World wide increment has been detected in atmospheric lightning related to the increase in surface air temperature, sea surface temperature and aerosol density. This work aims to an analysis of the relation between the annual courses of thunderstorms occurrence, the surface air temperature and the occurrence of haze and smoke reports at Casablanca meteorological station in Havana City, which has a very reliable series of tri-hourly observations for a period of 45 years. The thunderstorms series is also related, for a shorter period, to an aerosol index series. The study yields that the frequency of thunderstorms observations has increased by 5% for the period with a highly significant growing trend. Yearly average temperatures also show a highly significant increase and the best correlation is reached for the yearly frequency of occurrence of temperatures above 30°C, where 46% of the variance of thunderstorms occurrence is explained. The haze occurrence reports have also a highly significant trend and show a correlation of 0.65 with thunderstorms. Aerosol index has a growing trend for 2005 – 2016 and explains 58% of the variance of thunderstorms frequency.

The short term prediction of precipitation is a difficult spatio-temporal task due to the non-uniform characterization of meteorological structures over time. Currently, neural networks such as convolutional LSTM have shown ability for the spatio -temporal prediction of complex problems. In this research, it is proposed an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data. The CNN-LSTM is trained with NASA Global Precipitation Measurement (GPM) precipitation data sets, each at 30-minute intervals. The trained neural network model is used to predict the eleventh precipitation data of the corresponding ten precipitation sequence and up to a time interval of 120 minutes. The results show that the increase in the number of layers, as well as in the amount of data in the training data set, improves the quality in the forecast.

Precipitation measurement is essential for most environmental studies, such as drought monitoring, watershed operations and water hazard management. The development of satellite products has improved their applicability in environmental models and could offer an alternative to gauge-based precipitation data, particularly in areas where there is not a sufficient number of meteorological stations, but they need to be evaluated in different areas using data terrestrial as references. This research aims to carry out a validation of the product of Integrated Recoveries from Multiple Satellites (IMERG) of global precipitation measurement (GPM) for the network of meteorological stations in Havana, Cuba. The study focused on investigating the performance of GPM IMERG (Early) products every 30 minutes by comparing them with rain gauge data on land at meteorological stations (2014-2020). The performance of the GPM IMERG was evaluated using different interpolation methods and performing a statistical analysis. The results obtained allow to choose the best interpolating method, as well as to evaluate the temporal and spatial precision of the satellite data. Further work will expand the findings and methods to the Cuban meteorological stations network.

Government departments use the air quality index (AQI) to inform the public about how unhealthy the air is now or may become in the future. As the AQI increases, so do the health threats. It is a daily air quality index that is used to report on air quality. In addition, a measure of how air pollution impacts one's health over a limited period of time. The AQI was created to assist people in understanding how local air quality affects their health. Therefore the aim of the study was to assess one-day air quality of 253 towns in Nigeria, thereby determining the health threat in these towns. The data was collected from the Tutiempo Network's regular data set by the EPA Environmental Protection Agency. Data on all of the major pollutants (O₃, PM_2.5, PM₁₀, CO, NO, SO₂) was collected and statistical analysis was performed. Kura (Kano State), a town in northern Nigeria, recorded the highest level of 184, while Idiroko, a border town (Nigeria-Benin Republic) in Ogun State, had the least value of 41. Kura was portrayed as unhealthy, while Idiroko was portrayed as healthy, implying that Idiroko air poses little to no danger, while Kura air showed that certain people of the general public, as well as members of sensitive groups, could encounter more severe health effects.

Port-cities interested in busy shipping routes can have a significant environmental and health exposure risk to emissions related to harbour activities. However, the recent strengthening of the IMO legislation, in force since 2020, on the sulphur content in maritime fuels (from 3.5% to 0.5% in mass) is expected to curb ship emissions of sulphur oxides and particulate matter, thus reducing shipping-related mortality and morbidity.

Being more harmful to health, fine particles and nanoparticles, emitted by ships’ engines, could be monitored as particle number concentration (PNC), in addition to regulatory limits for PM₁₀ and PM_2.5.

In this work, an integrated approach based on high temporal resolution measurements of PM_2.5 and PNC was applied in two Italian harbour sites (Brindisi and Venice). Some experimental campaigns performed in different years (between 2012 and 2018) with a similar instrumental setup and statistical treatment of acquired particle concentration data, meteorological conditions and ship movements records, allowed to investigate temporal trends in shipping impact on the same areas. In addition, contribution to different particle sizes was estimated at both harbours.

Inter-annual trends of estimated impacts are reported and discussed. In general terms, the contribution to PNC are 3-4 times larger than that to PM_2.5. In Venice, the effectiveness of the combined application of the international legislation and local voluntary agreements was evident in lowering the primary impact of maritime transport, especially on PM_2.5 at local scale, although an increase of ship traffic volume. Instead, the weight of port logistics was demonstrated by an increase of the PNC contribution in Brindisi. Finally, the size-segregated impact showed a maximum relative contribution to nanoparticles, followed by a minimum for larger diameters (between 1 µm and 1.5 µm), and a growth in the coarse size range, likely related to harbour logistics activities.

Atmospheric deposition is a key process to improve the understanding of human impacts on the nitrogen biogeochemical cycle and its processes. In order to evaluate the nitrogen atmospheric deposition species we quantified the dissolved inorganic nitrogen (DIN = NH₄⁺ + NO₃^-) and dissolved organic nitrogen (DON) fluxes in Lavras, Minas Gerais, Brazil from May 2018 until April 2019. Our sampling site region in Minas Gerais has a strong influence of local sources, such as fertilizers production and application. Moreover, Lavras has 19% (107 km²) of its total area associated with crops plantation. We collected wet and bulk atmospheric deposition samples (n = 50) and analyzed by Kjeldahl method (Total Dissolved Nitrogen – TDN) and ionic chromatography (Dissolved Inorganic Nitrogen – DIN) analysis, respectively. Overall, our results showed that wet deposition of DON was dominant, accounting for 7.34 kg.ha⁻¹.yr⁻¹, while NH₄⁺ and NO₃^- fluxes were of 1.39 and 3.94 kg.ha⁻¹.yr⁻¹, respectively. In addition, the NO₃^-/NH₄⁺ molar ratio ranged from 1.33 to 33.7 and the pH values ranged from 5.66 to 6.08. The lower values of NO₃^-/NH₄⁺ were associated with an alkaline behavior, suggesting dominance of NH₃ in the atmosphere neutralization processes. We noted that DIN species fluxes, NH₄⁺ and NO₃^-, were concentrated between October 2018 and March 2019, thus less than 14% of NH₄⁺ and NO₃^- annual deposition were observed in the dry period (April to September). This pattern was associated with seasonal rainfall distribution in Lavras, and with the volatilization of NH₃ and NO_x species due to fertilizers applications. Regarding DON annual deposition, we observed that such distribution was also present, whereas in a less pronounced percentage (64% in the wet period), suggesting that different processes also play role in Lavras.

Precipitation, one of the key components of the hydrologic cycle varying over time and space and traditionally measured by rain gauges located above ground level which is the only way to have a direct physical estimate of precipitation where its data accuracy depends to the stations density to cover the spatiotemporal variation of precipitation properly. Meanwhile, precipitation measurement over complex topography and high land regions such as eastern part of Turkey has always been a great challenge in the recent decades. On the other side, satellite-based and numerical weather precipitation models output can be an alternative to fill this gap. Hence, the goal of this study is to evaluate the spatiotemporal stability and hydrologic utility of four precipitation products (ERA5, IMERGHHFv06, TMPA-3b42v7and PERSIANN) over Karasu river basin which is located in the eastern part of the country. Moreover, Kling Gupta Efficiency (KGE) including its correlation, bias and variability ratio components was used for direct comparison of precipitation products (PPs) with observed precipitation and the Hansen-Kuipers score (HK) has been used to assess the detectability strength of PPs for different precipitation events. In the same way, the hydrologic utility of PPs was tested by using a conceptual rainfall-runoff model under Kling Gupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE) metrics. Generally, all PPs show low performance for the direct comparison with observed data while their performance drastically increases for streamflow simulation. TMPA-3b42v7 is able to predict streamflow close to observed discharge with the KGE performance 0.84 while IMERGHHFv06 comes with the second-best performance (KGE; 0.76). Likewise, ERA5 present a close performance to IMERGHHFv6 and well reproduce streamflow comparing to PERSIANN for the entire period (2015-2019) in this study.

This study investigated the carbonaceous species [elemental carbon (EC), organic carbon (OC),water soluble organic carbon (WSOC)] along with the trace elements (Al, S, Ti, Mn, Fe, Cu, Zn, As, Br, Pb, Cr, F, Cl, Na, K, Mg, Ca, P) in PM₁₀ over megacity Delhi, India (collected from 2015-2019) to address the significant scientific issues (i.e., what are the directionality or pathway of these emissions; what are the possible emission sources, which are distressing the observation site; periodical variations in these emissions; whether the emissions are local, regional or trans-boundary). Integration of these problem are addressed using the statistical approaches potential source areas (PSA) [using hybrid modelling i.e. potential source contribution factor (PSCF)], conditional bivariate probability function (CBPF) and principal component analysis (PCA). A profile of higher concentration of PM₁₀ was observed during post-monsoon (322 ± 73 µg m^-3) followed by winter (271±87 µg m^-3) and low concentration during summer (221 ± 89 µg m^-3) followed by monsoon (167±93 µg m^-3). Further, seasonal PSCF and CBPF indicates local sources (highly polluted residential, traffic congestions and industrial emissions) and regional sources (Haryana, Punjab) dominancy during winter and post-monsoon seasons at the receptor site whereas during summer and monsoon along with local source and the regional, trans-boundaries (Indo-Gangatic plane,Pakistan, Afganistan and Bay of Bengal) air parcel pattern also contribute to the aerosol loading at the site. Moreover, PCA approach framed four common sources [crustal/road dust (RD), industrial emission (IE), fossil fuel combustion +Biomass burning (FCC+ BB),vehicular emission (VE)] with one mixed source over the sampling site of Delhi.