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Subsoils contain half of the total soil organic carbon (SOC) that is supposed to be relatively more persistent than that present in the topsoil. Improving SOC and total nitrogen (TN) stocks in croplands is crucial to mitigate climate change and ensuring food security. However, our insight into how the management practices and climatic variables influence stocks of SOC and TN, and crop grain yields in the soil profile is limited. In this study, we assessed the long‐term impacts of mineral and manure fertilizers on SOC and TN stocks at soil profile levels (up to 100 cm), and cropping system (wheat–maize–soybean) grain yields. Results indicated that in the top 0–40‐cm layers SOC and TN stocks were the highest in manure plus mineral fertilizers (MNPK) compared with control, that is, non‐fertilized control (CK). Conversely, compared with NPK, sole application of manure (M) clearly increased SOC stocks by 19%, 40%, and 39% and TN stocks by 51%, 105%, and 116% in 40–60, 60–80, and 80–100 cm, respectively (p < 0.05). Moreover, Pearson correlation revealed that climate variables, that is, mean annual temperature (MAT) affected both SOC and TN stocks in 0–40‐cm layers only of the soil profile. Our findings implicated that the sole application of manure (M) is vital to augment SOC and TN sequestration, particularly in the subsurface layers. However, trade‐offs between SOC and TN sequestration and crop yields should also need to be considered while making recommendations for SOC and TN stocks maintenance and increasing crop productivity in terms of management strategies. [ABSTRACT FROM AUTHOR]

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM 2.5 concentration (μgm−3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM 2.5 , temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM 2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM 2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R 2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen. • Relationship between air pollution, climate, and socioeconomic factors on COVID-19 cases. • Modeling of COVID-19 with PM2.5, climatic, socioeconomic factors in provincial cities. • Vulnerable PM 2.5 conc. identified for COVID-19 spread in provincial capital (Lahore). • Relationship of PM2.5, COVID-19 and climatic factors used to predict risky cities in 2021. [ABSTRACT FROM AUTHOR]

Several major cities that witnessed heavy air pollution by particulate matter (PM 2.5) concentration and nitrogen dioxide (NO 2) have contributed to high rate of infection and severity of the coronavirus disease (COVID-19) pandemic. Owing to the negative impact of COVID-19 on health and economy, it is imperative to predict the pandemic trend of the COVID-19 outbreak. Pakistan is one of the mostly affected countries by recent COVID-19 pandemic in terms of COVID-cases and economic crises. Like other several Asian countries to combat the virus impacts, Pakistan implemented non-pharmacological interventions (NPI), such as national lockdowns. The current study investigates the effect of major interventions across three out of four provinces of Pakistan for the period from the start of the COVID-19 in March 22, 2020 until June 30, 2020, when lockdowns were started to be eased. High-resolution data on NO 2 was recorded from Sentinel-5's Precursor spacecraft with TROPOspheric Monitoring Instrument (Sentinel-5P TROPOMI). Similarly, PM 2.5 data were collected from sampling sties to investigate possible correlation among these pollutants and COVID-19. In addition, growth and susceptible-infected-recovered (SIR) models utilizing time-series data of COVID-19 from February 26 to December 31, 2020, with- and without NPI that encompass the predicted number of infected cases, peak time, impact on the healthcare system and mortality in Pakistan. Maximum mean PM 2.5 concentration of 108 μgm−3 was recorded for Lahore with the range from 51 to 215 μgm−3, during strict lockdown (L), condition. This is three times higher than Pak-EPA and US-EPA and four times for WHO guidelines, followed by Peshawar (97.2 and 58 ± 130), Islamabad (83 and 158 ± 58), and Karachi (78 and 50 ± 140). The majority of sampling sites in Lahore showed NO 2 levels higher than 8.75E-5 (mol/m2) in 2020 compared to 2019 during "L" period. The susceptible-infected-recovered (SIR) model depicted a strong correlation (r) between the predicted and reported cases for Punjab (r = 0.79), Sindh (r = 0.91), Khyber Pakhtunkhwa (KPK) (r = 94) and Islamabad (r = 0.85). Findings showed that major NPI and lockdowns especially have had a large effect on minimizing transmission. Continued community intervention should be undertaken to keep transmission of SARS-CoV-2 under control in cities where higher incidence of COVID-19 cases until the vaccine is available. This study provides a methodological framework that if adopted can assist epidemiologist and policy makers to be well-prepared in advance in cities where PM 2.5 concentration and NO 2 levels are already high in order to minimize the potential risk of further spread of COVID-19 cases. • Geoinformation technologies to analyze COVID19 spread and impacts for Pakistan. • Explore relationships between COVID19, air pollution & community interventions. • SIR model was tested without non-pharmacologic interventions (NPI) and with NPI. • Community interventions impacted the spread of Covid-19 and air quality. • Pollution events were observed during the strictest lockdown over Pakistan major cities. [ABSTRACT FROM AUTHOR]