Regional impacts on air quality and health of changing a manufacturing facility's grid-boiler to a combined heat and power system.

Poor air quality is linked to numerous adverse health effects including strokes, heart attacks, and premature death. Improving energy efficiency in the industrial sector reduces air emissions and yields health benefits. One of these strategies, replacing an existing grid boiler (GB) with a combined heat and power (CHP) system, can improve a facility's energy efficiency but can also increase local air emissions, which in turn can affect health outcomes. Previous studies have considered air-emissions and health outcomes of CHP system installation at a single location, but few studies have investigated the regional air quality and health impacts of replacing an existing GB with new CHP system. This study estimates the emission changes and associated health impacts of this shift in 14 regions in the US, representing different electricity generation profiles. It assumes that one manufacturing facility in each region switches from an existing GB to a CHP system. The monetized annual US health benefits of shifting a single GB to a CHP in each of the 14 regions range from $-5.3 to 0.55 million (2022 USD), while including CHP emission control increases the benefits by 100-170% ($9,000 to 1.15 million (2022 USD)). This study also includes a sensitivity analysis, which suggests that the facility location (region, state, and county), boiler efficiency, and emission control of the CHP are key factors that would determine whether shifting from a GB to CHP system would result in health benefits or burdens.Implications: Combined heat and power (CHP) systems offer industrial facilities the opportunity to improve their energy efficiency and reduce greenhouse gas emissions. However, CHP systems also combust more fuel on site and can also increase local air emissions. This study evaluates how converting an existing grid boiler (GB) system to a CHP system (with or without emission control) affects local (from combustion) and regional emissions (from electricity consumption) and the associated health burdens in different US regions. A facility can use this study's analysis as an example for estimating the tradeoffs between local emission changes, regional emission changes, and health effects. It also provides a comparison between the incremental cost of adding SCR (compared to uncontrolled CHPs) and the NPV of the monetized health benefits associated with adding the SCR.

Comparison of the most likely low-emission electricity production systems in Estonia.

To meet targets for reducing greenhouse gas emissions, many countries, including Estonia, must transition to low-emission electricity sources. Based on current circumstances, the most likely options in Estonia are renewables with energy storage, oil shale power plants with carbon capture and storage (CCS), or the combination of renewables and either oil shale or nuclear power plants. Here we compare these different scenarios to help determine which would be the most promising based on current information. For the comparison we performed simulations to assess how various systems meet the electricity demand in Estonia and at what cost. Based on our simulation results and literature data, combining wind turbines with thermal power plants would provide grid stability at a more affordable cost. Using nuclear power to compliment wind turbines would lead to an overall levelized cost of electricity (LCOE) in the range of 68 to 150 EUR/MWh (median of 103 EUR/MWh). Using oil shale power plants with CCS would give a cost between 91 and 163 EUR/MWh (median of 118 EUR/MWh). By comparison, using only renewables and energy storage would have an LCOE of 106 to 241 EUR/MWh (median of 153 EUR/MWh).

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor.

Ozone (O3) is a potent oxidant associated with adverse health effects. Low-cost O3 sensors, such as metal oxide (MO) sensors, can complement regulatory O3 measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O3 although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O3 federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O3 concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O3 concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU's MO oxidizing species and temperature measurements and DAQ's solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R2 = 0.491), and the ANN exhibited good performance (R2 = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O3 for five months after the calibration period and the results showed moderate correlations (R2s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O3 levels.