Site selection for nature-based solutions for stormwater management in urban areas: An approach combining GIS and multi-criteria analysis

In recent years, particularly following the definition of the UN Sustainable Development Goals (SDGs) for 2030, Nature-Based Solutions (NBS) have gained considerable attention, capturing the interest of both the scientific community and policymakers committed to addressing urban environmental issues. However, the need for studies to guide decision-makers in identifying suitable locations for NBS implementation within urban stormwater management is evident. To address this gap, the present study employs a methodological approach grounded in multi-criteria analysis integrated with Geographic Information Systems (GIS) to identify areas with potential for NBS implementation. In this process, ten NBS were proposed and tested in the drainage area of a shallow tropical urban lake in Londrina, southern Brazil. Additionally, the study investigates areas hosting lower-income populations, a relevant aspect for public managers given the diverse economic subsidies required to implement NBS. Furthermore, the study incorporates a preliminary analysis that evaluates the potential ecosystem benefits to determine the most suitable NBS for a specific site. The result shows that all the ten analyzed NBS were deemed suitable for the study area. Rain barrels had the highest percentage coverage in the study area (37.1%), followed by tree pits (27.9%), and rain gardens (25.4%). Despite having the highest distribution in the basin area, rain barrels exhibited only moderate ecosystem benefits, prompting the prioritization of other NBS with more significant ecological advantages in the final integrated map. In summary, the methodology proposed showed to be a robust approach to selecting optimal solutions in densely populated urban areas.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil

OBJECTIVES

To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil.

METHODS

Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates.

RESULTS

Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19.

CONCLUSIONS

Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.

Deforestation drivers in the Brazilian Amazon: assessing new spatial predictors

Researches on the deforestation of the Amazon have gained prominence in the last recent years, mainly with the change in the policy regarding the facing of this phenomenon by the Brazilian government. Therefore, an understanding about the causes that pressure the occurrence of deforestation remains relevant and has a leading role in the world. Therefore, the aim of this study is to perform the analysis of the spatial variability of the reasons for the deforestation in the Amazon Biome, in Brazil, (2010-2019). To achieve this goal, 14 variables were selected, the choice and adjustment of the regression model were determined and a diagnosis was carried out in order to verify the most appropriate model. To achieve this purpose, a geographic database was structured in a geographic information system environment. The main results revealed that the adjusted R² of the Geographically Weighted Regression (GWR) was 0.96, that is, the GWR model explains 96% of the variations in deforestation. Therefore, it was observed a significant gain when using this model. In addition, it was also observed that the average variable of the number of oxen was, among those analyzed, the one that showed the highest correlation with deforestation. Thus, it was found that the livestock sector in southern Amazonia is the main economic agent that pressures large areas of deforestation, since stockfarming is practiced extensively. Finally, it was concluded that the municipalities with the largest areas of deforestation formed a cluster in the southern portion of the Amazon, in the arc of deforestation.

How mobility restrictions policy and atmospheric conditions impacted air quality in the State of São Paulo during the COVID-19 outbreak

Mobility restrictions are among actions to prevent the spread of the COVID-19 pandemic and have been pointed as reasons for improving air quality, especially in large cities. However, it is crucial to assess the impact of atmospheric conditions on air quality and air pollutant dispersion in the face of the potential variability of all sources. In this study, the impact of mobility restrictions on the air quality was analyzed for the most populous Brazilian State, São Paulo, severely impacted by COVID-19. Ground-based air quality data (PM₁₀, PM_2.5, CO, SO₂, NO_x, NO₂, NO, and O₃) were used from 50 automatic air quality monitoring stations to evaluate the changes in concentrations before (January 01 - March 25) and during the partial quarantine (March 16 - June 30). Rainfall, fires, and daily cell phone mobility data were also used as supplementary information to the analyses. The Mann-Whitney U test was used to assess the heterogeneity of the air quality data during and before mobility restrictions. In general, the results demonstrated no substantial improvements in air quality for most of the pollutants when comparing before and during restrictions periods. Besides, when the analyzed period of 2020 is compared with the year 2019, there is no significant air quality improvement in the São Paulo State. However, special attention should be given to the Metropolitan Area of São Paulo (MASP), due to the vast population residing in this area and exposed to air pollution. The region reached an average decrease of 29% in CO, 28% in NO_x, 40% in NO, 19% in SO₂, 15% in PM_2.5, and 8% in PM₁₀ concentrations during the mobility restrictions period compared to the same period in 2019. The only pollutant that showed an increase in concentration was ozone, with a 20% increase compared to 2019 during the mobility restrictions period. Before the mobility restrictions period, the region reached an average decrease of 30% in CO, 39% in NO_x, 63% in NO, 12% in SO₂, 23% in PM_2.5, 18% in PM₁₀, and 16% in O₃ concentrations when compared to the same period in 2019. On the other hand, Cubatão, a highly industrialized area, showed statistically significant increases above 20% for most monitored pollutants in both periods of 2020 compared to 2019. This study reinforces that the main driving force of pollutant concentration variability is the dynamics of the atmosphere at its various time scales. An abnormal rainy season, with above average rainfall before the restrictions and below average after it, generated a scenario in which the probable significant reductions in emissions did not substantially affect the concentration of pollutants.

Biodegradable CA/CPB electrospun nanofibers for efficient retention of airborne nanoparticles

The increase of the industrialization process brought the growth of pollutant emissions into the atmosphere. At the same time, the demand for advances in aerosol filtration is evolving towards more sustainable technologies. Electrospinning is gaining notoriety, once it enables to produce polymeric nanofibers with different additives and also the obtaining of small pore sizes and fiber diameters; desirable features for air filtration materials. Therefore, this work aims to evaluate the filtration performance of cellulose acetate (CA) nanofibers and cationic surfactant cetylpyridinium bromide (CPB) produced by electrospinning technique for retention of aerosol nanoparticles. The pressure drop and collection efficiency measurements of sodium chloride (NaCl) aerosol particles (diameters from 7 to 300 nm) were performed using Scanning Mobility Particle Sizer (SMPS). The average diameter of the electrospun nanofibers used was 239 nm, ranging from 113 to 398 nm. Experimental results indicated that the nanofibers showed good permeability (10^-11 m²) and high-efficiency filtration for aerosol nanoparticles (about 100 %), which can include black carbon (BC) and the new coronavirus. The pressure drop was 1.8 kPa at 1.6 cm s^-1, which is similar to reported for some high-efficiency nanofiber filters. In addition, it also retains BC particles present in air, which was about 90 % for 375 nm and about 60 % for the 880 nm wavelength. Finally, this research provided information for future designs of indoor air filters and filter media for facial masks with renewable, non-toxic biodegradable, and potential antibacterial characteristics.

Development of a spatialized atmospheric emission inventory for the main industrial sources in Brazil

In this work, atmospheric pollutant emissions of NO_x, SO_x, CO, particulate matter (PM), total organic compounds (TOC), and CO₂ from larger stationary sources of pollutants in Brazil were inventoried and spatialized over the whole Brazilian territory for the base year 2011. The developed inventory comprises a total of 16 refining units, 1730 thermoelectric power plants (TPPs), 96 cement industries, and 64 paper and cellulose industries. To obtain the dataset, some strategies were used, including mail contact, official datasets, personal requesting, web maps usage, and official industry websites. The emission factors were based on lower and upper limits proposed by the AP-42 standards of the US Environmental Protection Agency - USEPA, as well as, emission factors provided by air pollution control agencies, industries, and those identified in the scientific literature. The results show values of 857 ± 415 Gg/year for NO_x, 1.51 ± 1.23 Tg/year for SO_x, 21.2 ± 13.7 Tg/year for CO, 10.4 ± 10.1 Tg/year for PM, 1.14 ± 0.95 Tg/year for TOC, and 476 ± 142 Tg/year for CO₂. In comparison with the official vehicular emission inventory provided by the Ministry of Environment for the year 2011, the total NO_x emissions estimated in this work were slightly lower than vehicular emissions, while SO_x was 300 times greater than vehicular emissions. For CO, the stationary emissions inventoried were around 17 times greater than vehicular emissions, while PM was approximately 360 times greater than those from vehicles. In terms of comparison with existing global databases, the estimates of this work showed a good level of agreement with the pollutants estimated by the Global Emissions EDGAR v4.3.1, except for PM and CO, which were higher in our estimates. The major contribution of the proposed inventory lies in its improved spatialized distribution, higher resolution, and greater distinctness about the high level of uncertainty associated with the emission inventories for the region.

Impact of mining activities on areas of environmental protection in the southwest of the Amazon: A GIS- and remote sensing-based assessment

The southwest Brazilian Amazon state of Rondônia has a relatively recent non-indigenous occupation, which subsequently develops a variety of human pressures and conflicts of interest presently identified in the region. Given such framework, it is worthwhile to note that there are 57 Conservation Units that must guarantee the biodiversity protection of Amazonia biome. However, due to the need for electricity and the international high demand for minerals, the highlighted scenario has dramatically changed in recent years. Official data from mining processes in the studied area demonstrated the existence of high interest for minerals, especially cassiterite and gold. Mining is indeed an essential activity for the nation mainly due to the generation of jobs and income. On the other hand, it produces several environmental and social impacts that vary accordingly to the peculiarities of the mining and with respect to the type of ore. Therefore, this research work investigated the concentration of mining projects in the state of Rondônia and complementarily evaluated the impact of such activities on areas of environmental protection. Landsat 8 OLI imageries were employed to map the diversity of land covers across the study area and also to evaluate the corresponding impact of mining activities. More than 500 processes have been identified within the Conservation Units. A significant part of the mining areas, covering about 26 km², was observed inside one of the three types of evaluated areas (Integral Protection Sustainable Use Conservation Units and Damping Zone), clearly showing the development of the illegal activity, as defined in terms of the National System of Conservation Units. If deforestation is considered in this analysis, the area increases to about 6110 km², representing more than 5.2% of the Conservation Units. In addition, we proposed the creation of the Mining Pressure Index (MPI), which allows classifying a Conservation Unit by the degree of pressure from mining activities. The proposed index revealed to be very efficient since it predicted high values over Conservation Units where it was expected a greater vulnerability. The index is a promising tool for public policy formulation and management of protected areas, as well as for enforcement action. The results provide a new horizon in terms of the perspective of importance and applicability of geotechnologies in the evaluation of environmental impacts, not restricted to mining activity.