Algorithm developed for dynamic quantification of coal consumption for and emission from rural winter heating

Abstract Coal-dominated winter heating practices in China are largely accepted to be a leading cause of winter haze in the region though the amount of coal for heating is actually much lower than for power generation or industrial process. However, little is known about how the total rural coal weight in a region could be attributed to real time (e.g., daily) patterns, limiting the understanding of dynamic impacts of coal emissions and the adoption of timely measures against predicted haze. Considering that winter heating essentially protects against cold temperatures, coal burning strength may be related to the temperatures that people experience. A field study was organized to test the validity of this hypothesis. A system was designed to continuously monitor every instance of coal addition, and coal consumption on any given day for a whole village (WDAY) was calculated by summating all the additions. Meanwhile, a new term, composite temperature (TCOM), which incorporates a few weather-related elements, was introduced to represent cold temperatures that individuals experience. It was found that WDAY and TCOM presented opposite variations, and a negative linear correlation was observed (WDAY = −0.75TCOM + 11.86, R2 = 0.75), revealing the feasibility of estimating coal consumption on a certain day (WDAY) based on weather data (TCOM) for a given village. An extensive form of the algorithm for any area of interest (e.g., a district, city, or province) can be expressed as WDAY = (−0.75TCOM + 11.86)‧NH/834, where NH denotes the number of households in a region. This algorithm reflects the essence of winter heating (to resist cold temperatures), and therefore its logic is highly likely to be useful for any countries of the world regardless of what forms of energy used (coal or other energy forms) provided the energy involved is unexceptionally used for winter heating, though there may be some uncertainties in estimated coal consumption due to multiple factors.