The northeastern United States has experienced significant emissions reductions in the last two decades leading to a decrease in PM_2.5, major gaseous pollutants (SO₂, CO, NO_x) and ultrafine particles (UFPs) concentrations. Emissions controls were implemented for coal-fired power plants, and new heavy-duty diesel trucks were equipped with particle traps and NO_x control systems, and ultralow sulfur content is mandatory for both road and non-road diesel as well as residual oil for space heating. At the same time, economic changes also influenced the trends in air pollutants. Investigating the influence of these changes on ultrafine particle sources is fundamental to determine the success of the mitigation strategies and to plan future actions. Particle size distributions have been measured in Rochester, NY since January 2002. The particle sources were investigated with positive matrix factorization (PMF) of the size distributions (11–470 nm) and measured criteria pollutants during five periods (2002–2003, 2004–2007, 2008–2010, 2011–2013, and 2014–2016) and three seasons (winter, summer, and transition). Monthly, weekly, and hourly source contributions patterns were evaluated.

Lake Maurepas, Louisiana, holds ecological, recreational, and economic significance, but recent concerns have arisen over its water quality due to industrial activities. From June to November 2023, we investigated water and sediment quality at nine sites and three depths. Results showed that NH₃-N levels were within safety limits (0.11 ± 0.10 mg/L), while Total Nitrogen (TN, 0.83 ± 0.65 mg/L), Total Phosphorus (TP, 0.32 ± 0.13 mg/L), Chemical Oxygen Demand (COD, 25.94 ± 11.37 mg/L), Arsenic (As, 0.26 ± 0.17 mg/L), and Lead (Pb, 0.23 ± 0.002 mg/L) exceeded acceptable thresholds. Spatial-temporal analysis revealed significant variations across sites, depths, and sampling dates. Major contaminant sources included discharges from the Tickfaw, Amite, and Blind rivers, as well as a vehicle accident on Pass Manchac. Seismic and drilling activities by Air Products and Chemicals had little to no observed impact. Four AI algorithms were also evaluated using different physical parameter inputs to predict December’s chemical pollutant levels, which were missing due to adverse weather. The LSTM model outperformed the others, achieving R² values of 0.852 for COD, 0.869 for TN, 0.842 for As, and 0.921 for TP and Pb. Predictions indicated decreasing pollutant levels in December, which matched salinity and specific conductance measurements, and reverted to those observed in September and October. This pattern is attributed to the settling of contaminants from the Pass Manchac accident and ongoing pollutant sources from September and October.

The power of sensing molecules is often characterized in part by determining their thermodynamic/dynamic properties, in particular the binding constant of a guest to a host. In many studies, traditional nonlinear regression analysis has been used to determine the binding constants, which cannot be applied to complex systems and limits the reliability of such calculations. Supramolecular sensor systems include many interactions that make such chemical systems complicated. The challenges in creating sensing molecules can be significantly decreased through the availability of detailed mathematical models of such systems. Here, we propose uncovering accurate thermodynamic parameters of chemical reactions using better-defined mathematical modeling-fitting analysis is the key to understanding molecular assemblies and developing new bio/sensing agents. The supramolecular example we chose for this investigation is a self-assembled sensor consists of a synthesized receptor, DPA (DPA = dipicolylamine)-appended phenylboronic acid (1) in combination with Zn²⁺(1.Zn) that forms various assemblies with a fluorophore like alizarin red S (ARS). The self-assemblies can detect multi-phosphates like pyrophosphate (PPi) in aqueous solutions. We developed a mathematical model for the simultaneous quantitative analysis of twenty-seven intertwined interactions and reactions between the sensor (1.Zn-ARS) and the target (PPi) for the first time, relying on the Newton-Raphson algorithm. Through analyzing simulated potentiometric titration data, we describe the concurrent determination of thermodynamic parameters of the different guest-host bindings. Various values of temperatures, initial concentrations, and starting pHs were considered to predict the required measurement conditions for thermodynamic studies. Accordingly, we determined the species concentrations of different host-guest bindings in a generalized way. This way, the binding capabilities of a set of species can be quantitatively examined to systematically measure the power of the sensing system. This study shows analyzing supramolecular self-assemblies with solid mathematical models has a high potential for a better understanding of molecular interactions within complex chemical networks and developing new sensors with better sensing effects for bio-purposes.