Capture and Storage of Carbon in the Dry Forests of Pomac (Lambayeque, Peru) to Improve the Focus of Reforestation on New Ecosystem Services

The estimation of aerial forest carbon storage and capacity (CCS) is a fundamental instrument to evaluate the application of forest management measures and soil recovery under the approach of use in new ecosystem services (CCUS). The research has aimed to evaluate the capture and storage of carbon in the dry forests of Pomac (Lambayeque, Peru) to improve the approach of reforestation in new ecosystem services. For this, the non-destructive method was used, 15 random plots were selected, the tree species were identified, counted and measured, taking into account the diameter at breast height (DBH), the results indicated a total accumulation of aerial biomass ( dry) of 75.09 t/ha and a mass of stored carbon of 37,545 t/ha. The highest contribution of aerial biomass was dominated by three species: Prosopis pallida H.B.K. for Arnata ferreyra (carob tree) (90.99%) > Capparis ovalifolia (Vichayo) (6.13 %) > Capparis scabrida (Sapote) (2.02 %). The quadratic models to estimate the aerial biomass (AGB) of each species and of the plots studied based on the DBH were robust (R2>0.7) and significant and demonstrated a great payment potential in relation to the carbon credit in the Voluntary Market of Carbon. These results can be used to improve forest management and the recovery of degraded soils under the approach of new ecosystem uses that include the CCUS approach

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM10 for San Juan de Mirafores (SJM) (PM10-SJM: 78.7 µg/m3) and the lowest in Santiago de Surco (SS) (PM10 -SS: 40.2 µg/m3). The PCA showed the infuence of relative humidity (RH)-atmospheric pressure (AP)temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE < 0.3) and the NSE-MLR criterion (0.3804) was acceptable. PM10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.Campus San Juan de Luriganch

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM10 for San Juan de Miraflores (SJM) (PM10-SJM: 78.7 μ g/m3) and the lowest in Santiago de Surco (SS) (PM10-SS: 40.2 μ g/m3). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE < 0.3) and the NSE-MLR criterion (0.3804) was acceptable. PM10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic