(311a) Integrating Ecological Solutions with Technology for Enhanced Air Quality Control: A Spatio-Temporal Design and Operations Framework

Air pollution, a major global health concern, causes 4.2 million deaths annually [1]. Despite regulations and efforts to mitigate it, poor air quality persists in many areas. More than 43% of Americans are exposed to particulate matter and ground-level ozone, with vulnerable communities facing disproportionate exposure [2]. Ground-level ozone fluctuates rapidly on sunny days, while SO2 quality deteriorates in winter. Thus, designing for air quality regulation should account for spatio-temporal variability of pollution response function.

Industrial and chemical process emissions contribute greatly to air pollution. Hence, many engineers and researchers have developed pollution control technology and improved its efficiency. For example, Mcrae et al. [3] created a real-time control algorithm to regulate ground-level ozone concentration in Los Angeles. Guldmann [4] formulated a spatial optimization problem for minimizing pollutant exposure in city planning by determining emission stack locations and resident locations. Kerl et. al. [5] designed an Air Pollution Optimization Model (APOM) to mitigate health impacts from power plant emissions in Georgia. They optimized operations for a month with an hourly resolution using a spatio-temporal explicit pollution response function.

Meanwhile, the ecological restoration literature has studied the impact of forests on air pollution regulation. Ecosystems provide cost-effective pollution regulation services. Kroeger et al. [6] compared reforestation, selective catalytic reactors (SCR), and cap-and-trade schemes for ozone regulation in Houston, finding reforestation to be environmentally superior. Gopalakrishnan et al. [7] found reforestation to be cheaper for air quality regulation in most US counties. While these studies assumed constant ecosystem capacity, their promising results warrant further research. Accounting for ecosystems as unit operations can yield economically, environmentally, and socially beneficial solutions. Bakshi et al.[8] proposed a multi-scale techno-ecological synergy (TES) framework to explicitly consider ecosystem capacity and operate in harmony with nature.

Charles and Bakshi [9] formulated a spatial land use optimization problem for TES systems to regulate air quality using reforestation. They assumed constant emission and ignored temporal variation. In contrast, Shah and Bakshi [10, 11] tackled a simultaneous design and operations optimization problem that considered temporal variation in ecological capacity but not spatial variation, finding net-positive manufacturing solutions. They extended their framework to real-time ecological capacity variation adaptation [11]. Existing models and literature lack adequate consideration of spatio-temporal variability in simultaneous design and operation of air quality regulation systems, leading to sub-optimal and impractical solutions.

The spatial and temporal variability of air pollution effects have not been adequately considered in existing models and literature. This can lead to the oversight of air pollution hotspots and sub-optimal nature based solution. Spatio-temporal air quality modeling is necessary for accurate assessment of the ability of nature based solution to regulate and abate air pollution. This work develops an algorithm for a spatio-temporally explicit TES simultaneous design and operations optimization framework for air quality regulation. This work builds on a previous model developed by Shah and Bakshi [11] for temporally explicit TES problems.

This work aims to address this gap by developing an algorithm for simultaneous design and operations optimization for both technological and nature-based solutions. The algorithm considers spatial and temporal fluctuations, enabling a more accurate and holistic approach. This framework builds on Shah and Bakshi’s [11] temporally explicit TES model.

To demonstrate our approach, we consider a chloralkali manufacturing facility in Miami Township, Ohio. The facility has an onsite coal-fired generator that emits pollutants like sulfur dioxide. We focus on regulating sulfur dioxide in 2016. We compare a flue gas desulphurizer (FGD) and reforestation as alternatives for mitigating sulfur dioxide emissions. We set up a spatio-temporally explicit simultaneous design and operations problem to determine the FGD unit size, reforestation location, and hourly production schedule of chlorine and FGD utilization rate. We aim to minimize production costs while meeting hourly air quality constraints.

This paper addresses the simultaneous optimization of design and operations for a chlor-alkali plant in a dynamic environment. The problem is decomposed into a design problem and an operations optimization problem, which are solved iteratively using a simulation-optimization framework. Our analysis involves three sequential steps: obtaining design parameters using Bayesian optimization [12], conducting spatio-temporal simulation of air pollution transport using CALPUFF [13], and simulating optimal operations using a dynamic mathematical model. Our results show that the proposed approach can effectively optimize both the location of reforestation and operations of the chlor-alkali plant, resulting in significant economic and ecological cost savings.

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