The temporal variability in NO, NO₂, NO_x, CO₂, and O₃ concentrations was investigated from 2007bb to 2015 at an extensive urban wetland area, characterized by (i) wetlands and marshes; (ii) an urban interface; (iii) hydrological features; (iv) environmental challenges; and (v) biodiversity and conservation. Here, we utilized commercial Thermo Fisher Instruments Inc. trace gas analyzers to measure temporal distribution of O₃, NO, NO/NO_x, and CO₂.from 2007 to 2015 at a convergence zone between the I-95, New Jersey, and New York City. The diurnal profiles of NO and NO₂ are dominated by photochemical destruction during the day, lack thereof during the nighttime, and a buildup in the morning due to rush hour traffic with a low amount of solar irradiance. The CO2 profile shows loss during the day due to photosynthetic activity, anthropogenic release during the early morning hours, and buildup during the night due to anthropogenic release. O₃ is anticorrelated to NO_x, as NO and NO₂ are precursor species for photochemical O3 production during the daytime; ozone’s gradual decrease during the nighttime and sudden dip in the early morning are primarily governed by a lack of/low solar irradiance during the night and early morning, respectively. The monthly and yearly mean profiles showcase the longer timescale representation of the diurnal profiles. Ordinary linear regression (OLR) modeling revealed minor but nonnegligible correlations with temperature, RH, solar irradiance, wind speed and direction. The OLR exhibited a negative dependence on ln[X] (where X = CO₂, NO, NO₂, or NO_x) as a function of wind speed and temperature. O₃ displayed an increasingly positive dependence as the temperature and wind speed increased. In contrast, CO₂, NO, NO₂, and NO_x exhibited positive dependences as a function of RH, while O₃ displayed negative dependences on RH. Pearson, Kendall, and Spearman correlation analyses complement OLR analyses, as CO₂, NO, NO₂, NO_x, and O₃ exhibit minimal correlations with solar irradiance, temperature, RH, wind speed and direction. Moreover, OLR-normalized correlation analyses also highlight the importance of higher-resolution data; here, we show that more data are significant at higher resolutions than daily and weekly data. This 8-year measurement study complements the national and statewide trend of gradual decreases in emissions of air quality pollutants and carbon dioxide. This study reinforces the importance of continuous environmental and assessment activities, which are benchmarks for air quality and climate simulations/forecasts and policy development and implementation (e.g., State Implementation Plans, SIPS).

Reinforced by this study, New York City has one of the cleanest water systems in the world. Medgar Evers College (MEC) serves 7000 students/1050 faculty/staff. Given that: (1) students/faculty/staff spend 20–30% of their daily time there; (2) potable water sources must abide by the EPA’s maximum contamination levels (MCLs); and (3) a detrimental impact on human health arises from violations to EPA’s water quality mandates, we quantified the abundance of 27 heavy metals (96 samples, N = 3) using MEC as a case study. Water was collected from all potable water sources, following EPA protocols for sample-matrix preparation, collection, and wet-chemical analysis. Linear polyethylene containers/caps were used to prevent sample contamination while the water samples were spiked with HNO₃ (aq) for preservation. Heavy metal concentrations were quantified using New Jersey’s Meadowlands Environmental Research Institute’s Inductively Coupled Plasma-Mass Spectrometer (ICP-MS, Agilent 7700X) in no gas, and He flow modes. Ninety-five percent of sample concentration relative standard deviations (RSDs) reveal four distinct regions: (1) where one mode is more precise than the other, and sample data exhibit very good to excellent precision, RSD ≤ 15%; (2) despite being at low concentrations, measurements exhibit good to excellent precision, RSD ≤ 20%; (3) species concentrations ≥0.1 ppb very good to excellent precision is shown, RSD ≤ 15%; and (4) species at concentrations ≤ 10⁻³ ppb display fair to very poor precision, RSD ≥ 30%. All heavy metals complied with their respective EPA MCLs (except Fe). Over 90% of Fe sample concentrations were enhanced by up to about 30×. Two samples exhibited [Pb] = 13.7 (No gas mode, RSD = 3.32%) and 14.8 ppb (He mode, RSD = 0.75%), which is close to the EPA Primary MCL, 15 ppb. Based on EPA/WHO end-member equations, we estimate a 1/103 to 1/108 chance of cancer attainment from long-term exposure to the range of concentrations of heavy metals measured in this study.

The study analyzed urban growth (UG) and land surface temperature (LST) of Asaba and Abuja in Nigeria from 1986 to 2022. Landsat 5 TM imagery of 1986, Landsat 7 ETM of 2000, and Landsat OLI/TIRS of 2022, of 30m resolutions, were obtained for the determination of both LST and UG analyses. The UG analysis was subjected to the supervised classification of the maximum likelihood module to generate the different land use/ land cover of the study area. Descriptive statistics involving the frequency and percentage were used for the data analysis. Findings from the study revealed that the mean LST, which was 33.38 °C in 1986, decreased to 28.25 in 2002 and subsequently 28.98oC in 2022, while the built-up Area representing urban development increased from 14% in 1986 to 65% in 2022. The UG was significantly increasing at the rate of 242% while LST increased at 7 °C within the period under consideration. The study concludes that the increase in the built-up area is responsible for the increase in the land surface temperature in Asaba and Abuja Metropolis. The authors recommend that LST parameters be taken into consideration for sustainable urban planning to be achieved in the city. Policymakers at all levels need to scale up measures to mitigate the adverse impact of urban heat islands by suggesting a clustered greening of the entire urban landscape. The study has also provided pathways in addressing the complexity of land surface temperature and the future urban development of the city of Asaba and Abuja.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM_2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM_2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM_2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM_2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM_2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM_2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM_2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.