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— In this paper, we first develop a low-cost surfacebased air pollutants measurement system for the real-time air pollution monitoring and forecasting applications. Then, we compare the performance achieved by the proposed system in real-time urban environment with currently used static monitoring stations by the governmental environmental protection agency (EPA). The proposed design uses particulate matter, humidity, and temperature sensors to measure the values of the air pollutant that determines the value of the Air Quality Index (AQI). The SD storage device is interfaced with the system to store the large amount of data sensed by the system. The Arduino UNO-based processing unit integrates with the sensing units to process and control the sensed air pollutants data. The proposed system is deployed in indoors and outdoor environment in under served minority communities in big cities to illustrate real-time environmental pollution measurement and monitoring applications. The system can measure, monitor and alert the level of PM2.5 and PM10 components of the AQI as they are often the main pollutant that determines the AQI value. The performance of the proposed system compares with the expensive data logger-based EPA-approved LDEQ sensorsbased air quality monitoring system. Our analysis shows that the measurement and monitoring performance of the proposed system is comparable with the EPA-approved LDEQ sensorsbased air quality monitoring system. The analysis also shows that there is a spatial and temporal variation of PM2.5 and PM10 values even for sites that are less than a mile apart. The interaction interphase of the system is simpler and easier to use as compared with bulky display systems in traditional EPA-based monitoring systems. In contrast with the traditional data logger-based system, the proposed system is smaller and quicker to deploy to test specific air pollutants in interested urban and rural locations . 

This study presents a two-stage stochastic aggregate production planning model to determine the optimal renewable generation capacity, production plan, workforce levels, and machine hours that minimize a production system’s operational cost. The model considers various uncertainties, including demand for final products, machine and labor hours available, and renewable power supply. The goal is to evaluate the feasibility of decarbonizing the manufacturing, transportation, and warehousing operations by adopting onsite wind turbines and solar photovoltaics coupled with battery systems assuming the facilities are energy prosumers. First-stage decisions are the siting and sizing of wind and solar generation, battery capacity, production quantities, hours of labor to keep, hire, or layoff, and regular, overtime, and idle machine hours to allocate over the planning horizon. Second-stage recourse actions include storing products in inventory, subcontracting or backorder, purchasing or selling energy to the main grid, and daily charging or discharging energy in the batteries in response to variable generation. Climate analytics performed in San Francisco and Phoenix permit to derive capacity factors for the renewable energy technologies and test their implementation feasibility. Numerical experiments are presented for three instances: island microgrid without batteries, island microgrid with batteries, and grid-tied microgrid for energy prosumer. Results show favorable levelized costs of energy that are equal to USD48.37/MWh, USD64.91/MWh, and USD36.40/MWh, respectively. The model is relevant to manufacturing companies because it can accelerate the transition towards eco-friendly operations through distributed generation.