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Olena Voloshkina

Publications and source records attributed to Olena Voloshkina.

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Modeling Indoor PM$_{2.5}$ Exposure During Retrofits: Plastic Film Barriers and a Quadratic Baseline Approach

Temporary plastic film barriers are widely used to separate occupied rooms from exterior renovation zones, yet their effect on indoor particulate exposure is poorly quantified. We monitored PM$_{2.5}$ in a Tampa, Florida, apartment for 48 days with a low-cost optical sensor (Temtop LKC-1000S+), spanning pre-barrier, barrier-on, and post-barrier periods. A quadratic baseline was fitted to "background" minutes devoid of identifiable indoor sources, allowing excess concentrations ($Δ$PM) to be partitioned into facade work, cooking, and passive accumulation without outdoor co-monitoring. The barrier prevented large construction spikes indoors but curtailed natural ventilation, doubling the mean baseline from 1.9 to 3.9 $μ$g m$^{-3}$. During this stage, passive build-up accounted for $45\,\%$ of the daily excess dose, with facade work and cooking contributing $31\,\%$ and $24\,\%$, respectively. Once the new window was installed and evening airing resumed, the baseline fell to 0.8 $μ$g m$^{-3}$, the lowest of the campaign. Our findings highlight the trade-off between dust shielding and background elevation and demonstrate that simple polynomial fitting bolsters low-cost IAQ diagnostics in mechanically unventilated dwellings. The framework is readily transferable to other retrofits; future studies should pair indoor sensing with outdoor references and multi-room deployments to refine infiltration estimates.

physics.med-ph

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.

stat.ME