Particle filters (PFs) form a class of Monte Carlo algorithms that propagate over time a set of $N\geq 1$ particles which can be used to estimate, in an online fashion, the sequence of filtering distributions $(\hatη_t)_{t\geq 1}$ defined by a state-space model. Despite the popularity of PFs, the study of the time evolution of their estimates has received barely any attention in the literature. Denoting by $(\hatη_t^N)_{t\geq 1}$ the PF estimate of $(\hatη_t)_{t\geq 1}$ and letting $κ\in (0,1/2)$, in this work we first show that for any number of particles $N$ it holds that, with probability one, we have $\|\hatη_t^N- \hatη_t\|\geq κ$ for infinitely many time instants $t\geq 1$, with $\|\cdot\|$ the Kolmogorov distance between probability distributions. Considering a simple filtering problem we then provide reassuring results concerning the ability of PFs to estimate jointly a finite set $\{\hatη_t\}_{t=1}^T$ of filtering distributions by studying the probability $\mathbb{P}(\sup_{t\in\{1,\dots,T\}}\|\hatη_t^{N}-\hatη_t\|\geq κ)$. Finally, on the same toy filtering problem, we prove that sequential quasi-Monte Carlo, a randomized quasi-Monte Carlo version of PF algorithms, offers greater safety guarantees than PFs in the sense that, for this algorithm, it holds that $\lim_{N\rightarrow\infty}\sup_{t\geq 1}\|\hatη_t^N-\hatη_t\|=0$ with probability one.