{'fraction_non_alphanumeric': 0.012130401819560273, 'fraction_numerical': 0.0, 'mean_word_length': 5.502463054187192, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Marked Point Process Model for the Source Proximity Effect in the Indoor Environment

In indoor air quality studies, discrepancies between personal and stationary indoor air quality monitors arise because of the source proximity effect, in which pollutant sources near the respondent cause elevated and highly variable exposures. In a set of experiments in a home, concentrations of a continuously emitting tracer gas were simultaneously monitored at different distances from the tracer gas source. Concentration time series are modeled at collinear monitoring sites as the sum of a slowly varying baseline time series and the superposition of transient elevated concentrations, or “microplumes.” Microplume arrivals appear as pulses in the time series, with pulse magnitudes and duration varying by location relative to the source. A nonparametric method is developed to estimate the time-varying parameters of the baseline time series. Parameters of superposed microplumes are estimated using the method of moments. Bias and sampling error of estimates are investigated using a simulation study. Estimates of superposition model parameters provide insight into the physical reasons behind the source proximity effect as well as a description of components of exposure at different distances from an emitting source.